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How to Create an APA Table of Contents | Format & Examples

Published on November 5, 2020 by Jack Caulfield . Revised on January 17, 2024.

A table of contents is not required in an APA Style paper , but if you include one, follow these guidelines:

• Include all level 1 and level 2 headings (other levels are optional).
• Indicate different heading levels with indents. Adhere to general APA format in terms of font, spacing, etc.

You can automatically create the table of contents by applying APA heading styles in Word.

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Table of contents

Apa format guidelines for the table of contents, how to generate a table of contents in word.

In a thesis or dissertation , the table of contents comes between your abstract and your introduction . It should be written in the same font and size as the rest of your text (usually 12 pt Times New Roman). At the top of the page, write Contents , centered and in bold.

In APA Style, you can use up to five levels of heading , each with its own formatting style. In the table of contents, you should include all level 1 and 2 headings, left-aligned and formatted as plain text. Level 2 headings are indented.

Including lower-level headings in the table of contents is optional. Add an additional indent for each level. If you have a lot of headings in your text, you may not be able to include them all—your table of contents should not be more than two pages long in total.

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To automatically generate a table of contents in Word, you’ll first have to apply heading styles throughout your text. After that, you can generate the table of contents.

Applying heading styles

First, go through your text making sure that each level of heading is in keeping with APA Style rules.

Next, update the heading styles listed in the Home tab at the top:

• Highlight a level 1 heading
• Right-click the Heading 1 style and select Update Heading 1 to match selection
• Do the same for each level of heading

Once you’ve done this you can update any other headings quickly using the heading styles. Make sure all headings are in the appropriate style before proceeding.

Generating the table of contents

Now you can generate your table of contents. First write the title “Contents” (in the style of a level 1 heading). Then place your cursor two lines below this and go to the References tab.

Click on Table of Contents and select Custom Table of Contents… In the popup window, select how many levels of heading you wish to include (at least two) under Show levels , then click OK :

Updating your table of contents

Now you have a table of contents based on your current headings and page numbers. If you continue working on your text after this, be sure to go back and update your table of contents at the end, as headings and page numbers might change.

You can do this by right-clicking on the table of contents and selecting Update Field . Then you can choose whether to update all information or just the page numbers. It’s best to update everything, just to be sure.

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Caulfield, J. (2024, January 17). How to Create an APA Table of Contents | Format & Examples. Scribbr. Retrieved April 15, 2024, from https://www.scribbr.com/apa-style/apa-table-of-contents/

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• Writing Tips

How to Structure the Table of Contents for a Research Paper

4-minute read

• 16th July 2023

So you’ve made it to the important step of writing the table of contents for your paper. Congratulations on making it this far! Whether you’re writing a research paper or a dissertation , the table of contents not only provides the reader with guidance on where to find the sections of your paper, but it also signals that a quality piece of research is to follow. Here, we will provide detailed instructions on how to structure the table of contents for your research paper.

Steps to Create a Table of Contents

• Insert the table of contents after the title page.

Within the structure of your research paper , you should place the table of contents after the title page but before the introduction or the beginning of the content. If your research paper includes an abstract or an acknowledgements section , place the table of contents after it.

• List all the paper’s sections and subsections in chronological order.

Depending on the complexity of your paper, this list will include chapters (first-level headings), chapter sections (second-level headings), and perhaps subsections (third-level headings). If you have a chapter outline , it will come in handy during this step. You should include the bibliography and all appendices in your table of contents. If you have more than a few charts and figures (more often the case in a dissertation than in a research paper), you should add them to a separate list of charts and figures that immediately follows the table of contents. (Check out our FAQs below for additional guidance on items that should not be in your table of contents.)

• Paginate each section.

Label each section and subsection with the page number it begins on. Be sure to do a check after you’ve made your final edits to ensure that you don’t need to update the page numbers.

• Format your table of contents.

The way you format your table of contents will depend on the style guide you use for the rest of your paper. For example, there are table of contents formatting guidelines for Turabian/Chicago and MLA styles, and although the APA recommends checking with your instructor for formatting instructions (always a good rule of thumb), you can also create a table of contents for a research paper that follows APA style .

• Add hyperlinks if you like.

Depending on the word processing software you’re using, you may also be able to hyperlink the sections of your table of contents for easier navigation through your paper. (Instructions for this feature are available for both Microsoft Word and Google Docs .)

To summarize, the following steps will help you create a clear and concise table of contents to guide readers through your research paper:

1. Insert the table of contents after the title page.

2. List all the sections and subsections in chronological order.

3. Paginate each section.

4. Format the table of contents according to your style guide.

5. Add optional hyperlinks.

If you’d like help formatting and proofreading your research paper , check out some of our services. You can even submit a sample for free . Best of luck writing your research paper table of contents!

What is a table of contents?

A table of contents is a listing of each section of a document in chronological order, accompanied by the page number where the section begins. A table of contents gives the reader an overview of the contents of a document, as well as providing guidance on where to find each section.

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What should I include in my table of contents?

If your paper contains any of the following sections, they should be included in your table of contents:

●  Chapters, chapter sections, and subsections

●  Introduction

●  Conclusion

●  Appendices

●  Bibliography

Although recommendations may differ among institutions, you generally should not include the following in your table of contents:

●  Title page

●  Abstract

●  Acknowledgements

●  Forward or preface

If you have several charts, figures, or tables, consider creating a separate list for them that will immediately follow the table of contents. Also, you don’t need to include the table of contents itself in your table of contents.

Is there more than one way to format a table of contents?

Yes! In addition to following any recommendations from your instructor or institution, you should follow the stipulations of your style guide .

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Dissertation Structure & Layout 101: How to structure your dissertation, thesis or research project.

By: Derek Jansen (MBA) Reviewed By: David Phair (PhD) | July 2019

So, you’ve got a decent understanding of what a dissertation is , you’ve chosen your topic and hopefully you’ve received approval for your research proposal . Awesome! Now its time to start the actual dissertation or thesis writing journey.

To craft a high-quality document, the very first thing you need to understand is dissertation structure . In this post, we’ll walk you through the generic dissertation structure and layout, step by step. We’ll start with the big picture, and then zoom into each chapter to briefly discuss the core contents. If you’re just starting out on your research journey, you should start with this post, which covers the big-picture process of how to write a dissertation or thesis .

*The Caveat *

In this post, we’ll be discussing a traditional dissertation/thesis structure and layout, which is generally used for social science research across universities, whether in the US, UK, Europe or Australia. However, some universities may have small variations on this structure (extra chapters, merged chapters, slightly different ordering, etc).

So, always check with your university if they have a prescribed structure or layout that they expect you to work with. If not, it’s safe to assume the structure we’ll discuss here is suitable. And even if they do have a prescribed structure, you’ll still get value from this post as we’ll explain the core contents of each section.  

Overview: S tructuring a dissertation or thesis

• Acknowledgements page
• Abstract (or executive summary)
• Table of contents , list of figures and tables
• Chapter 1: Introduction
• Chapter 2: Literature review
• Chapter 3: Methodology
• Chapter 4: Results
• Chapter 5: Discussion
• Chapter 6: Conclusion
• Reference list

As I mentioned, some universities will have slight variations on this structure. For example, they want an additional “personal reflection chapter”, or they might prefer the results and discussion chapter to be merged into one. Regardless, the overarching flow will always be the same, as this flow reflects the research process , which we discussed here – i.e.:

• The introduction chapter presents the core research question and aims .
• The literature review chapter assesses what the current research says about this question.
• The methodology, results and discussion chapters go about undertaking new research about this question.
• The conclusion chapter (attempts to) answer the core research question .

In other words, the dissertation structure and layout reflect the research process of asking a well-defined question(s), investigating, and then answering the question – see below.

To restate that – the structure and layout of a dissertation reflect the flow of the overall research process . This is essential to understand, as each chapter will make a lot more sense if you “get” this concept. If you’re not familiar with the research process, read this post before going further.

Right. Now that we’ve covered the big picture, let’s dive a little deeper into the details of each section and chapter. Oh and by the way, you can also grab our free dissertation/thesis template here to help speed things up.

The title page of your dissertation is the very first impression the marker will get of your work, so it pays to invest some time thinking about your title. But what makes for a good title? A strong title needs to be 3 things:

• Succinct (not overly lengthy or verbose)
• Specific (not vague or ambiguous)
• Representative of the research you’re undertaking (clearly linked to your research questions)

Typically, a good title includes mention of the following:

• The broader area of the research (i.e. the overarching topic)
• The specific focus of your research (i.e. your specific context)
• Indication of research design (e.g. quantitative , qualitative , or  mixed methods ).

For example:

A quantitative investigation [research design] into the antecedents of organisational trust [broader area] in the UK retail forex trading market [specific context/area of focus].

Again, some universities may have specific requirements regarding the format and structure of the title, so it’s worth double-checking expectations with your institution (if there’s no mention in the brief or study material).

Acknowledgements

This page provides you with an opportunity to say thank you to those who helped you along your research journey. Generally, it’s optional (and won’t count towards your marks), but it is academic best practice to include this.

So, who do you say thanks to? Well, there’s no prescribed requirements, but it’s common to mention the following people:

• Your dissertation supervisor or committee.
• Any professors, lecturers or academics that helped you understand the topic or methodologies.
• Any tutors, mentors or advisors.
• Your family and friends, especially spouse (for adult learners studying part-time).

There’s no need for lengthy rambling. Just state who you’re thankful to and for what (e.g. thank you to my supervisor, John Doe, for his endless patience and attentiveness) – be sincere. In terms of length, you should keep this to a page or less.

Abstract or executive summary

The dissertation abstract (or executive summary for some degrees) serves to provide the first-time reader (and marker or moderator) with a big-picture view of your research project. It should give them an understanding of the key insights and findings from the research, without them needing to read the rest of the report – in other words, it should be able to stand alone .

For it to stand alone, your abstract should cover the following key points (at a minimum):

• Your research questions and aims – what key question(s) did your research aim to answer?
• Your methodology – how did you go about investigating the topic and finding answers to your research question(s)?
• Your findings – following your own research, what did do you discover?
• Your conclusions – based on your findings, what conclusions did you draw? What answers did you find to your research question(s)?

So, in much the same way the dissertation structure mimics the research process, your abstract or executive summary should reflect the research process, from the initial stage of asking the original question to the final stage of answering that question.

In practical terms, it’s a good idea to write this section up last , once all your core chapters are complete. Otherwise, you’ll end up writing and rewriting this section multiple times (just wasting time). For a step by step guide on how to write a strong executive summary, check out this post .

Need a helping hand?

Table of contents

This section is straightforward. You’ll typically present your table of contents (TOC) first, followed by the two lists – figures and tables. I recommend that you use Microsoft Word’s automatic table of contents generator to generate your TOC. If you’re not familiar with this functionality, the video below explains it simply:

If you find that your table of contents is overly lengthy, consider removing one level of depth. Oftentimes, this can be done without detracting from the usefulness of the TOC.

Right, now that the “admin” sections are out of the way, its time to move on to your core chapters. These chapters are the heart of your dissertation and are where you’ll earn the marks. The first chapter is the introduction chapter – as you would expect, this is the time to introduce your research…

It’s important to understand that even though you’ve provided an overview of your research in your abstract, your introduction needs to be written as if the reader has not read that (remember, the abstract is essentially a standalone document). So, your introduction chapter needs to start from the very beginning, and should address the following questions:

• What will you be investigating (in plain-language, big picture-level)?
• Why is that worth investigating? How is it important to academia or business? How is it sufficiently original?
• What are your research aims and research question(s)? Note that the research questions can sometimes be presented at the end of the literature review (next chapter).
• What is the scope of your study? In other words, what will and won’t you cover ?
• How will you approach your research? In other words, what methodology will you adopt?
• How will you structure your dissertation? What are the core chapters and what will you do in each of them?

These are just the bare basic requirements for your intro chapter. Some universities will want additional bells and whistles in the intro chapter, so be sure to carefully read your brief or consult your research supervisor.

If done right, your introduction chapter will set a clear direction for the rest of your dissertation. Specifically, it will make it clear to the reader (and marker) exactly what you’ll be investigating, why that’s important, and how you’ll be going about the investigation. Conversely, if your introduction chapter leaves a first-time reader wondering what exactly you’ll be researching, you’ve still got some work to do.

Now that you’ve set a clear direction with your introduction chapter, the next step is the literature review . In this section, you will analyse the existing research (typically academic journal articles and high-quality industry publications), with a view to understanding the following questions:

• What does the literature currently say about the topic you’re investigating?
• Is the literature lacking or well established? Is it divided or in disagreement?
• How does your research fit into the bigger picture?
• How does your research contribute something original?
• How does the methodology of previous studies help you develop your own?

Depending on the nature of your study, you may also present a conceptual framework towards the end of your literature review, which you will then test in your actual research.

Again, some universities will want you to focus on some of these areas more than others, some will have additional or fewer requirements, and so on. Therefore, as always, its important to review your brief and/or discuss with your supervisor, so that you know exactly what’s expected of your literature review chapter.

Now that you’ve investigated the current state of knowledge in your literature review chapter and are familiar with the existing key theories, models and frameworks, its time to design your own research. Enter the methodology chapter – the most “science-ey” of the chapters…

In this chapter, you need to address two critical questions:

• Exactly HOW will you carry out your research (i.e. what is your intended research design)?
• Exactly WHY have you chosen to do things this way (i.e. how do you justify your design)?

Remember, the dissertation part of your degree is first and foremost about developing and demonstrating research skills . Therefore, the markers want to see that you know which methods to use, can clearly articulate why you’ve chosen then, and know how to deploy them effectively.

Importantly, this chapter requires detail – don’t hold back on the specifics. State exactly what you’ll be doing, with who, when, for how long, etc. Moreover, for every design choice you make, make sure you justify it.

In practice, you will likely end up coming back to this chapter once you’ve undertaken all your data collection and analysis, and revise it based on changes you made during the analysis phase. This is perfectly fine. Its natural for you to add an additional analysis technique, scrap an old one, etc based on where your data lead you. Of course, I’m talking about small changes here – not a fundamental switch from qualitative to quantitative, which will likely send your supervisor in a spin!

You’ve now collected your data and undertaken your analysis, whether qualitative, quantitative or mixed methods. In this chapter, you’ll present the raw results of your analysis . For example, in the case of a quant study, you’ll present the demographic data, descriptive statistics, inferential statistics , etc.

Typically, Chapter 4 is simply a presentation and description of the data, not a discussion of the meaning of the data. In other words, it’s descriptive, rather than analytical – the meaning is discussed in Chapter 5. However, some universities will want you to combine chapters 4 and 5, so that you both present and interpret the meaning of the data at the same time. Check with your institution what their preference is.

Now that you’ve presented the data analysis results, its time to interpret and analyse them. In other words, its time to discuss what they mean, especially in relation to your research question(s).

What you discuss here will depend largely on your chosen methodology. For example, if you’ve gone the quantitative route, you might discuss the relationships between variables . If you’ve gone the qualitative route, you might discuss key themes and the meanings thereof. It all depends on what your research design choices were.

Most importantly, you need to discuss your results in relation to your research questions and aims, as well as the existing literature. What do the results tell you about your research questions? Are they aligned with the existing research or at odds? If so, why might this be? Dig deep into your findings and explain what the findings suggest, in plain English.

The final chapter – you’ve made it! Now that you’ve discussed your interpretation of the results, its time to bring it back to the beginning with the conclusion chapter . In other words, its time to (attempt to) answer your original research question s (from way back in chapter 1). Clearly state what your conclusions are in terms of your research questions. This might feel a bit repetitive, as you would have touched on this in the previous chapter, but its important to bring the discussion full circle and explicitly state your answer(s) to the research question(s).

Next, you’ll typically discuss the implications of your findings? In other words, you’ve answered your research questions – but what does this mean for the real world (or even for academia)? What should now be done differently, given the new insight you’ve generated?

Lastly, you should discuss the limitations of your research, as well as what this means for future research in the area. No study is perfect, especially not a Masters-level. Discuss the shortcomings of your research. Perhaps your methodology was limited, perhaps your sample size was small or not representative, etc, etc. Don’t be afraid to critique your work – the markers want to see that you can identify the limitations of your work. This is a strength, not a weakness. Be brutal!

This marks the end of your core chapters – woohoo! From here on out, it’s pretty smooth sailing.

The reference list is straightforward. It should contain a list of all resources cited in your dissertation, in the required format, e.g. APA , Harvard, etc.

It’s essential that you use reference management software for your dissertation. Do NOT try handle your referencing manually – its far too error prone. On a reference list of multiple pages, you’re going to make mistake. To this end, I suggest considering either Mendeley or Zotero. Both are free and provide a very straightforward interface to ensure that your referencing is 100% on point. I’ve included a simple how-to video for the Mendeley software (my personal favourite) below:

Some universities may ask you to include a bibliography, as opposed to a reference list. These two things are not the same . A bibliography is similar to a reference list, except that it also includes resources which informed your thinking but were not directly cited in your dissertation. So, double-check your brief and make sure you use the right one.

The very last piece of the puzzle is the appendix or set of appendices. This is where you’ll include any supporting data and evidence. Importantly, supporting is the keyword here.

Your appendices should provide additional “nice to know”, depth-adding information, which is not critical to the core analysis. Appendices should not be used as a way to cut down word count (see this post which covers how to reduce word count ). In other words, don’t place content that is critical to the core analysis here, just to save word count. You will not earn marks on any content in the appendices, so don’t try to play the system!

Time to recap…

And there you have it – the traditional dissertation structure and layout, from A-Z. To recap, the core structure for a dissertation or thesis is (typically) as follows:

• Acknowledgments page

Most importantly, the core chapters should reflect the research process (asking, investigating and answering your research question). Moreover, the research question(s) should form the golden thread throughout your dissertation structure. Everything should revolve around the research questions, and as you’ve seen, they should form both the start point (i.e. introduction chapter) and the endpoint (i.e. conclusion chapter).

I hope this post has provided you with clarity about the traditional dissertation/thesis structure and layout. If you have any questions or comments, please leave a comment below, or feel free to get in touch with us. Also, be sure to check out the rest of the  Grad Coach Blog .

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36 Comments

many thanks i found it very useful

Glad to hear that, Arun. Good luck writing your dissertation.

Such clear practical logical advice. I very much needed to read this to keep me focused in stead of fretting.. Perfect now ready to start my research!

what about scientific fields like computer or engineering thesis what is the difference in the structure? thank you very much

Thanks so much this helped me a lot!

Very helpful and accessible. What I like most is how practical the advice is along with helpful tools/ links.

Thanks Ade!

Thank you so much sir.. It was really helpful..

You’re welcome!

Hi! How many words maximum should contain the abstract?

Thank you so much 😊 Find this at the right moment

You’re most welcome. Good luck with your dissertation.

best ever benefit i got on right time thank you

Many times Clarity and vision of destination of dissertation is what makes the difference between good ,average and great researchers the same way a great automobile driver is fast with clarity of address and Clear weather conditions .

I guess Great researcher = great ideas + knowledge + great and fast data collection and modeling + great writing + high clarity on all these

You have given immense clarity from start to end.

Morning. Where will I write the definitions of what I’m referring to in my report?

Thank you so much Derek, I was almost lost! Thanks a tonnnn! Have a great day!

Thanks ! so concise and valuable

This was very helpful. Clear and concise. I know exactly what to do now.

Thank you for allowing me to go through briefly. I hope to find time to continue.

Really useful to me. Thanks a thousand times

Very interesting! It will definitely set me and many more for success. highly recommended.

Thank you soo much sir, for the opportunity to express my skills

Usefull, thanks a lot. Really clear

Very nice and easy to understand. Thank you .

That was incredibly useful. Thanks Grad Coach Crew!

My stress level just dropped at least 15 points after watching this. Just starting my thesis for my grad program and I feel a lot more capable now! Thanks for such a clear and helpful video, Emma and the GradCoach team!

Do we need to mention the number of words the dissertation contains in the main document?

It depends on your university’s requirements, so it would be best to check with them 🙂

Such a helpful post to help me get started with structuring my masters dissertation, thank you!

Great video; I appreciate that helpful information

It is so necessary or avital course

This blog is very informative for my research. Thank you

Doctoral students are required to fill out the National Research Council’s Survey of Earned Doctorates

wow this is an amazing gain in my life

This is so good

How can i arrange my specific objectives in my dissertation?

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• Academic essay overview
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Table of Contents

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A guide to the table of contents page

Inhaltsverzeichnis

• 1 Definition: Table of Contents
• 3 Everything for Your Thesis
• 5 Create in Microsoft Word
• 6 In a Nutshell

Definition: Table of Contents

The table of contents is an organized listing of your document’s chapters, sections and, often, figures, clearly labelled by page number. Readers should be able to look at your table of contents page and understand immediately how your paper is organized, enabling them to skip to any relevant section or sub-section. The table of contents should list all front matter, main content and back matter, including the headings and page numbers of all chapters and the bibliography . A good table of contents should be easy to read, accurately formatted and completed last so that it is 100% accurate. Although you can complete a table of contents manually, many word processing tools like Microsoft Word enable you to format your table of contents automatically.

When adding the finishing touches to your dissertation, the table of contents is one of the most crucial elements. It helps the reader navigate (like a map) through your argument and topic points. Adding a table of contents is simple and it can be inserted easily after you have finished writing your paper. In this guide, we look at the do’s and don’ts of a table of contents; this will help you process and format your dissertation in a professional way.

When adding the finishing touches to your dissertation, the table of contents is one of the most crucial elements. It helps the reader navigate (like a map) through your argument and topic points. Adding a table of contents is simple and can be inserted easily after you have finished writing your paper. In this guide, we look at the do’s and don’ts of a table of contents; this will help you process and format your dissertation in a professional way.

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What is a table of contents?

A table of contents is a list, usually on a page at the beginning of a piece of academic writing , which outlines the chapters or sections names with their corresponding page numbers. In addition to chapter names, it includes bullet points of the sub-chapter headings or subsection headings. It usually comes right after the title page of a research paper.

How do you write a table of contents

To write a table of contents, you first write the title or chapter names of your research paper in chronological order. Secondly, you write the subheadings or subtitles, if you have them in your paper. After that, you write the page numbers for the corresponding headings and subheadings. You can also very easily set up a table of contents in Microsoft Word.

Where do you put a table of contents?

The table of contents is found on a page right at the beginning of an academic writing project. It comes specifically after the title page and acknowledgements, but before the introductory page of a writing project. This position at the beginning of an academic piece of writing is universal for all academic projects.

What to include in a table of contents?

A sample table of contents includes the title of the paper at the very top, followed by the chapter names and subtitles in chronological order. At the end of each line, is the page number of the corresponding headings. Examples of chapter names can be: executive summary,  introduction, project description, marketing plan, summary and conclusion. The abstract and acknowledgments are usually not included in the table of contents, however this could depend on the formatting that is required by your institution. Scroll down to see some examples.

How important is a table of contents?

A table of contents is very important at the beginning of a writing project for two important reasons. Firstly, it helps the reader easily locate contents of particular topics itemized as chapters or subtitles. Secondly, it helps the writer arrange their work and organize their thoughts so that important sections of an academic project are not left out. This has the extra effect of helping to manage the reader’s expectation of any academic essay or thesis right from the beginning.

Everything for Your Thesis

A table of contents is a crucial component of an academic thesis. Whether you’re completing a Bachelor’s or a postgraduate degree, the table of contents is a requirement for dissertation submissions. As a rule of thumb, your table of contents will usually come after your title page , abstract, acknowledgement or preface. Although it’s not necessary to include a reference to this front matter in your table of contents, different universities have different policies and guidelines.

Although the table of contents is best completed after you have finished your thesis, it’s a good idea to draw up a mock table of contents in the early stages of writing. This allows you to formulate a structure and think through your topic and how you are going to research, answer and make your argument. Think of this as a form of “reverse engineering”. Knowing how your chapters are going to be ordered and what topics or research questions are included in each will help immensely when it comes to your writing.

The table of contents is not just an academic formality, it allows your examiner to quickly get a feel for your topic and understand how your dissertation will be presented. An unclear or sloppy table of contents may even have an adverse effect on your grade because the dissertation is difficult to follow.

Examiners are readers, after all, and a dissertation is an exercise in producing an argument. A clear table of contents will give both a good impression and provide an accurate roadmap to make the examiner’s job easier and your argument more persuasive.

Your table of contents section will come after your acknowledgements and before your introduction. It includes a list of all your headers and their respective pages and will also contain a sub-section listing your tables, figures or illustrations (if you are using them). In general, your thesis can be ordered like this:

1. Title Page 2. Copyright / Statement of Originality 3. Abstract 4. Acknowledgement, Dedication and Preface (optional) 5. Table of Contents 6. List of Figures/Tables/Illustrations 7. Chapters 8. Appendices 9. Endnotes (depending on your formatting) 10. Bibliography / References

The formatting of your table of contents will depend on your academic field and thesis length. Some disciplines, like the sciences, have a methodical structure which includes recommended subheadings on methodology, data results, discussion and conclusion. Humanities subjects, on the other hand, are far more varied. Whichever discipline you are working in, you need to create an organized list of all chapters in their order of appearance, with chapter subheadings clearly labelled.

Sample table of contents for a short dissertation:

Abstract ………………………………………………………………………………………………….. ii Acknowledgements ………………………………………………………………………………………………….. iii Dedication ………………………………………………………………………………………………….. iv List of Tables ………………………………………………………………………………………………….. x List of Figures ………………………………………………………………………………………………….. xi Chapter 1: Introduction ………………………………………………………………………………………………….. 1 Chapter 2: Literature Survey ………………………………………………………………………………………………….. 13 Chapter 3: Methodology ………………………………………………………………………………………………….. 42 Chapter 4: Analysis ………………………………………………………………………………………………….. 100 Chapter 5: Conclusion ………………………………………………………………………………………………….. 129 Appendices ………………………………………………………………………………………………….. 169 References ………………………………………………………………………………………………….. 172

When producing a more significant and longer dissertation, say for a Master’s degree or even a PhD, your chapter descriptions should contain all subheadings. These are listed with the chapter number, followed by a decimal point and the subheading number.

Sample table of contents for a PhD dissertation:

Chapter 1 1.1 Introduction 1.2 Literature Review 1.3 Data 1.4 Findings 1.5 Conclusion

Chapter 2, and so on.

The key to writing a good table of contents is consistency and accuracy. You cannot list subheadings for one chapter and forget them for another. Subheadings are not always required but they can be very helpful if you are dealing with a detailed topic. The page numbers in the table of contents must match with the respective pages in your thesis or manuscript.

What’s more, chapter titles and subheading titles must match their corresponding pages. If your first chapter is called “Chapter 1: The Beginning”, it must be written as such on both the table of contents and first chapter page. So long as you remain both accurate and consistent, your table of contents will be perfect.

Create in Microsoft Word

Fortunately, the days of manually writing a contents page are over. You can still produce a contents page manually with Microsoft Word, but consider using their automatic feature to guarantee accuracy and save time.

To produce an automatically-generated table of contents, you must first work with heading styles. These can be found in the home tab under “Styles”. Select top-level headings (your chapter titles) and apply the Heading 1 style. This ensures that they will be formatted as main headings. Second-level headings (subheadings) can be applied with the Heading 2 style. This will place them underneath and within each main heading.

Once you have worked with heading styles, simply click on the “References” tab and select “Table of Contents”. This option will allow you to automatically produce a page with accurate page links to your document. To customize the format and style applied to your table of contents, select “Custom Table of Contents” at the bottom of the tab. Remember to update your table of contents by selecting the table and choosing “Update” from the drop-down menu. This will ensure that your headings, sub-headings and page numbers all add up.

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In a Nutshell

• The table of contents is a vital part of any academic thesis or extensive paper.
• It is an accurate map of your manuscript’s content – its headings, sub-headings and page numbers.
• It shows how you have divided your thesis into more manageable chunks through the use of chapters.
• By breaking apart your thesis into discrete sections, you make your argument both more persuasive and easier to follow.
• What’s more, your contents page should produce an accurate map of your thesis’ references, bibliography, illustrations and figures.
• It is an accurate map of the chapters, references, bibliography, illustrations and figures in your thesis.

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Microsoft Word for Dissertations

• Table of Contents
• Introduction, Template, & Resources
• Formatting for All Readers
• Applying a Style
• Modifying a Style
• Setting up a Heading 1 Example
• Images, Charts, Other Objects
• Footnotes, Endnotes, & Citations
• Cross-References
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• List of Figures/Tables
• Chapter and Section Numbering
• Page Numbers
• Landscape Pages
• Combining Chapter Files
• Commenting and Reviewing
• The Two-inch Top Margin
• Troubleshooting
• Finalizing Without Styles
• Preparing Your Final Document

Automatic Table of Contents

An automatic Table of Contents relies on Styles to keep track of page numbers and section titles for you automatically. Microsoft Word can scan your document and find everything in the Heading 1 style and put that on the first level of your table of contents, put any Heading 2’s on the second level of your table of contents, and so on.

If you want an automatic table of contents you need to apply the Heading 1 style to all of your chapter titles and front matter headings (like “Dedication” and “Acknowledgements”).  All section headings within your chapters should use the Heading 2  style.  All sub-section headings should use  Heading 3 , etc....

If you have used Heading styles in your document, creating an automatic table of contents is easy.

• Place your cursor where you want your table of contents to be.
• On the References Ribbon, in the Table of Contents Group , click on the arrow next to the Table of Contents icon, and select  Custom Table of Contents .
• We suggest that you set each level (Chapters, sections, sub-sections, aka TOC 1, TOC 2, TOC 3) to be single-spaced, with 12 points of space afterwards.  This makes each item in your ToC clump together if they're long enough to wrap to a second line, with the equivalent of a double space between each item, and makes the ToC easier to read and understand than if every line were double-spaced. See the video below for details.
• If you want to change which headings appear in your Table of Contents, you can do so by changing the number in the Show levels: field. Select "1" to just include the major sections (Acknowledgements, List of Figures, Chapters, etc...).  Select "4" to include Chapters, sections, sub-sections, and sub-sub-sections.
• Click OK to insert your table of contents.  

The table of contents is a snapshot of the headings and page numbers in your document, and does not automatically update itself as you make changes. At any time, you can update it by right-clicking on it and selecting Update field .  Notice that once the table of contents is in your document, it will turn gray if you click on it. This just reminds you that it is a special field managed by Word, and is getting information from somewhere else.

Modifying the format of your Table of Contents

The video below shows how to make your Table of Contents a little easier to read by formatting the spacing between items in your Table of Contents. You may recognize the "Modify Style" window that appears, which can serve as a reminder that you can use this window to modify more than just paragraph settings. You can modify the indent distance, or font, or tab settings for your ToC, just the same as you may have modified it for Styles. 

By default, the Table of Contents tool creates the ToC by pulling in Headings 1 through 3. If you'd like to modify that -- to only show H1's, or to show Headings 1 through 4 -- then go to the References tab and select Custom Table of Contents .  In the window that appears, set Show Levels to "1" to only show Heading 1's in the Table of Contents, or set it to "4" to show Headings 1 through 4.

Bonus tip for updating fields like the Table of Contents

You'll quickly realize that all of the automatic Lists and Tables need to be updated occasionally to reflect any changes you've made elsewhere in the document -- they do not dynamically update by themselves. Normally, this means going to each field, right-clicking on it and selecting "Update Field". 

Alternatively, to update all fields throughout your document (Figure/Table numbers & Lists, cross-references, Table of Contents, etc...), just select "Print". This will cause Word to update everything in anticipation of printing. Once the print preview window appears, just cancel.

Preparing a Title Page, Abstract and Table of Contents Page

• First Online: 04 November 2018

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• Kiruthika Sivasubramanian 3 ,
• Rajive Mathew Jose 4 &
• Stuart Enoch 5  

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The Title page, Abstract page and Table of Contents are the opening pages of a dissertation and therefore, essential to create a good impression on the reader.

The first page of a dissertation should succinctly summarise your work.

The abstract also called as the ‘executive summary’, follows the Title page and Declaration Statement and should encapsulate the essence of the dissertation.

The Table of Contents lists the contents contained within the dissertation with pagination helping the examiners and readers to find the information easily.

A stunning first impression was not the same thing as love at first sight. But surely it was an invitation to consider the matter.—Lois McMaster Bujold

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Sivasubramanian, K., Jose, R.M., Enoch, S. (2018). Preparing a Title Page, Abstract and Table of Contents Page. In: Parija, S., Kate, V. (eds) Thesis Writing for Master's and Ph.D. Program. Springer, Singapore. https://doi.org/10.1007/978-981-13-0890-1_12

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Thesis / dissertation formatting manual (2024).

• Filing Fees and Student Status
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• Dedication Page

Table of Contents

• List of Figures (etc.)
• Acknowledgements
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The Table of Contents should follow these guidelines:

• ​All sections of the manuscript are listed in the Table of Contents except the Title Page, the Copyright Page, the Dedication Page, and the Table of Contents.
• You may list subsections within chapters
• Creative works are not exempt from the requirement to include a Table of Contents

Table of Contents Example

Here is an example of a Table of Contents page from the Template. Please note that your table of contents may be longer than one page.

• << Previous: Dedication Page
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Choosing & Using Sources: A Guide to Academic Research

Table of Contents

Chapter 1: research questions.

• The Purpose of Research Questions
• Narrowing a Topic
• Background Reading
• Regular vs. Research Questions
• Influence of a Research Question
• Developing Your Research Question

Chapter 2: Types of Sources

• Categorizing Sources
• Quantitative or Qualitative
• Fact or Opinion
• Primary, Secondary & Tertiary Sources
• Popular, Professional, & Scholarly
• Publication Formats and the Information Cycle
• Scholarly Articles as Sources
• News as a Source
• Data as Sources

Chapter 3: Sources and Information Needs

• Sources and Information Needs
• Sources to Meet Needs
• Your Source Plan

Chapter 4: Precision Searching

• Why Precision Searching?
• Main Concepts
• Related Terms
• Search Statements

Chapter 5: Search Tools

• Library Catalog
• WorldCat@OSU
• Google Scholar
• Specialized Databases
• Web Search Engines
• Tips for Common Search Tools

Chapter 6: Evaluating Sources

• Thinking Critically About Sources
• Evaluating Websites
• A Source's Neighborhood
• Author and Publisher
• Degree of Bias
• Recognition from Others
• Currency of the Content
• Thoroughness
• Combining the Factors

Chapter 7: Ethical Use of Sources

• Ethical Use and Citing Sources
• Why Cite Sources?
• Other Challenges in Citing Sources

Chapter 8: How to Cite Sources

• Citation and Citation Styles
• Steps for Citing
• Citation Software
• When to Cite

Chapter 9: Making an Argument

• The Purpose of Academic Argument
• Components of an Argument
• Order of the Components
• Where You Get the Components
• Your Argument Plan

Chapter 10: Writing Tips

• Synthesis of Your Own Ideas
• When to Quote, Paraphrase, or Summarize
• Helping Others Follow

Chapter 11: Copyright Basics

• What Is Copyright?
• What Copyright Covers
• Rights Granted by Copyright
• Respecting Copyright
• Exceptions to Copyright
• Creative Commons: An Alternative
• Public Domain and Term of Copyright

Chapter 12: Fair Use

• What Is Fair Use?
• The Four Factors
• Evaluating Your Case for Fair Use
• Common Examples of Fair Use

Chapter 13: Roles of Research Sources

• Thinking About Roles of Sources
• BEAM—A Solution That Might Shine
• Using BEAM: An Example
• Practice with BEAM

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• Dissertation Table of Contents in Word | Instructions & Examples

Dissertation Table of Contents in Word | Instructions & Examples

Published on 15 May 2022 by Tegan George .

The table of contents is where you list the chapters and major sections of your thesis, dissertation, or research paper, alongside their page numbers. A clear and well-formatted table of contents is essential, as it demonstrates to your reader that a quality paper will follow.

The table of contents (TOC) should be placed between the abstract and the introduction. The maximum length should be two pages. Depending on the nature of your thesis, dissertation, or paper, there are a few formatting options you can choose from.

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Table of contents

What to include in your table of contents, what not to include in your table of contents, creating a table of contents in microsoft word, table of contents examples, updating a table of contents in microsoft word, other lists in your thesis, dissertation, or research paper, frequently asked questions about the table of contents.

Depending on the length of your document, you can choose between a single-level, subdivided, or multi-level table of contents.

• A single-level table of contents only includes ‘level 1’ headings, or chapters. This is the simplest option, but it may be too broad for a long document like a dissertation.
• A subdivided table of contents includes chapters as well as ‘level 2’ headings, or sections. These show your reader what each chapter contains.
• A multi-level table of contents also further divides sections into ‘level 3’ headings. This option can get messy quickly, so proceed with caution. Remember your table of contents should not be longer than 2 pages. A multi-level table is often a good choice for a shorter document like a research paper.

Examples of level 1 headings are Introduction, Literature Review, Methodology, and Bibliography. Subsections of each of these would be level 2 headings, further describing the contents of each chapter or large section. Any further subsections would be level 3.

In these introductory sections, less is often more. As you decide which sections to include, narrow it down to only the most essential.

Including appendices and tables

You should include all appendices in your table of contents. Whether or not you include tables and figures depends largely on how many there are in your document.

If there are more than three figures and tables, you might consider listing them on a separate page. Otherwise, you can include each one in the table of contents.

• Theses and dissertations often have a separate list of figures and tables.
• Research papers generally don’t have a separate list of figures and tables.

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All level 1 and level 2 headings should be included in your table of contents, with level 3 headings used very sparingly.

The following things should never be included in a table of contents:

• Your acknowledgements page
• Your abstract
• The table of contents itself

The acknowledgements and abstract always precede the table of contents, so there’s no need to include them. This goes for any sections that precede the table of contents.

To automatically insert a table of contents in Microsoft Word, be sure to first apply the correct heading styles throughout the document, as shown below.

• Choose which headings are heading 1 and which are heading 2 (or 3!
• For example, if all level 1 headings should be Times New Roman, 12-point font, and bold, add this formatting to the first level 1 heading.
• Highlight the level 1 heading.
• Right-click the style that says ‘Heading 1’.
• Select ‘Update Heading 1 to Match Selection’.
• Allocate the formatting for each heading throughout your document by highlighting the heading in question and clicking the style you wish to apply.

Once that’s all set, follow these steps:

• Add a title to your table of contents. Be sure to check if your citation style or university has guidelines for this.
• Place your cursor where you would like your table of contents to go.
• In the ‘References’ section at the top, locate the Table of Contents group.
• Here, you can select which levels of headings you would like to include. You can also make manual adjustments to each level by clicking the Modify button.
• When you are ready to insert the table of contents, click ‘OK’ and it will be automatically generated, as shown below.

The key features of a table of contents are:

• Clear headings and subheadings
• Corresponding page numbers

Check with your educational institution to see if they have any specific formatting or design requirements.

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Write yourself a reminder to update your table of contents as one of your final tasks before submitting your dissertation or paper. It’s normal for your text to shift a bit as you input your final edits, and it’s crucial that your page numbers correspond correctly.

It’s easy to update your page numbers automatically in Microsoft Word. Simply right-click the table of contents and select ‘Update Field’. You can choose either to update page numbers only or to update all information in your table of contents.

In addition to a table of contents, you might also want to include a list of figures and tables, a list of abbreviations and a glossary in your thesis or dissertation. You can use the following guides to do so:

• List of figures and tables
• List of abbreviations

It is less common to include these lists in a research paper.

All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.

The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .

Do not include the acknowledgements or abstract   in the table of contents.

To automatically insert a table of contents in Microsoft Word, follow these steps:

• Apply heading styles throughout the document.
• In the references section in the ribbon, locate the Table of Contents group.
• Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
• Select which levels of headings you would like to include in the table of contents.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

The table of contents in a thesis or dissertation always goes between your abstract and your introduction.

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• Puerto Rican Population Declines on Island, Grows on U.S. Mainland
• Chapter 1: Puerto Ricans on the U.S. Mainland

Table of Contents

• Chapter 2: Island Hispanics of Puerto Rican Origin
• Chapter 3: Recent and Previous Puerto Rican Migrants
• Appendix A: Additional Tables
• Appendix B: Maps of the U.S. Mainland and Puerto Rico
• Appendix C: Data Sources

The population of Hispanics of Puerto Rican origin on the U.S. mainland has more than doubled since 1980, rising to 4.9 million in 2012 from 2 million. The 2012 total alone was 45% higher than 2000’s 3.4 million. Still, the Puerto Rican origin population has grown less rapidly than U.S. Hispanics overall, whose numbers more than tripled from 1980 to 2012 and grew by half from 2000 to 2012.

Puerto Rican origin population growth has been powered mainly by an increase in those born in the 50 states or the District of Columbia, rather than by the increasing influx of migrants from the island. Mainland-born Puerto Ricans made up 69% of stateside Puerto Ricans in 2012, and their share has grown in recent years. This group has more than tripled in size since 1980 (1 million to 3.4 million), and grew 21% from 2007 to 2012.

On the other hand, the number of island-born Puerto Ricans on the mainland, 1.4 million in 2012, grew by only 51% from 1980 to 2012, and by 2% from 2007 to 2012. In 2012, island-born Puerto Ricans made up 29% of all stateside Puerto Ricans, down from about half in 1980.

There are notable differences in the characteristics of mainland-born and island-born Puerto Ricans living stateside. (See Appendix A tables for more details.) For example, the share of young, middle-aged and older people among all Puerto Ricans on the mainland and among other U.S. Hispanics is about the same. But there are notably fewer children and more elderly among island-born Puerto Ricans than among their mainland-born counterparts and other Hispanics.

In terms of language skills, Puerto Ricans (83%) are more likely than other U.S. mainland Hispanics (66%) to be proficient in English, meaning that they speak it very well or only speak English at home. The overall number is driven by high English proficiency among those born in the 50 states or District of Columbia (95%), compared with lower proficiency among the island born (60%).

Education and Economics

Puerto Ricans overall have more education than other U.S. Hispanics, and those born on the mainland have higher education levels than those born on the island.

Among Puerto Ricans living in the 50 U.S. states and District of Columbia, a higher share of adults ages 25 and older has attended or graduated from college (48%) than is true for other U.S. Hispanics. A lower share has no more than a high school education (52% to 64% in 2012).

As with other characteristics explored in this report, there are differences in education levels between mainland-born and island-born Puerto Ricans who live stateside. Mainland-born Puerto Ricans are more likely to have at least some college experience (54% versus 39%) and less likely to have a high school diploma or less (46% versus 61%). Overall, 18% of mainland-born Puerto Ricans have a bachelor’s degree compared with 15% of island-born Puerto Ricans.

Economically, mainland Puerto Ricans overall are less well off than other Hispanics, with median household incomes in 2012 of $41,400, compared with $46,000 for other Latinos. Mainland-born Puerto Ricans actually have higher median household incomes than other Hispanics ($47,840 in 2012). By contrast, the median household incomes among island-born Puerto Ricans are lower, at $34,500 in 2012. Puerto Ricans overall have a lower homeownership rate (38%) than other Hispanics (47%), but there is little difference for Puerto Ricans by birthplace. The poverty rate of Puerto Ricans (27% in 2012) is slightly higher than for other U.S. Hispanics (25%). Overall poverty rates are similar for mainland-born and island-born Puerto Ricans.

Geographic Distribution

Half of the nation’s Puerto Rican origin population lives in the Northeast (52% in 2012), 30% live in the South and the remainder is split between the Midwest and West. 7

Puerto Ricans dominate the Hispanic population in some Northeast states. As a share of all Hispanics, Puerto Ricans are the majority origin group in Connecticut (54.8%) and Pennsylvania (54.1%). Puerto Ricans are the largest origin group (though not a majority) among all Hispanics in Hawaii, Massachusetts, New Jersey and New York. 8 They are the second largest origin group in Florida, after Cubans.

The local impact of these trends can be seen in a Pew Research analysis of data for the nation’s roughly 360 counties with at least a thousand Puerto Rican residents in 2010 .

Only five counties out of those with at least a thousand Puerto Rican residents—all in the greater New York metropolitan area—experienced declines in their Puerto Rican populations from 2000 to 2010. Four—Bronx, Kings, New York and Queens counties—are in New York state, and the fifth was Hudson County, N.J. Of the five counties with sharpest growth in their Puerto Rican populations during this period, three are in the South—Union County, N.C.; Paulding County, Ga.; and Forsyth County, Ga. The others were Pinal County, Ariz., and Kendall County, Ill.

Puerto Rican Migration and Dispersion

The South was the top regional destination for recent moves by Hispanics of Puerto Rican origin from the island to the mainland, according to a Pew Research analysis of American Community Survey estimates for 2006 to 2012. Among all 334,000 moves during that period, 48% were to the South, including 31% to Florida, the state that attracted the largest share. New York, the next most popular state, accounted for 10% of recent moves off the island by Hispanics of Puerto Rican origin. 9

Among all Puerto Ricans who relocated from one region to another from 2006 to 2012, the South again was the top regional destination. The South was the first-choice region for the majority of moves by Puerto Ricans leaving the Northeast (81%), Midwest (56%) and West (60%).

New York and Florida were the most likely destinations that Puerto Ricans moved to from other states. About one-in-five moves (20%) were to New York and one-in-seven (14%) to Florida. About three-in-ten (31%) moves to Florida were from New York, and about the same share (29%) was in the other direction. Texas, which some research (Garcia-Ellin, 2014) has pinpointed as a new destination state for Puerto Ricans, especially the mainland born, attracted movers from many states and accounted for 6% of moves during the 2006 to 2012 period. The highest share (21%) came from Florida, while 9% came from both New York and California.

• The U.S. Census Bureau defines the U.S. regions as 1) Northeast: Connecticut, Massachusetts, Maine, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont; 2) Midwest: Iowa, Illinois, Indiana, Kansas, Michigan, Minnesota, Missouri, North Dakota, Nebraska, Ohio, South Dakota, Wisconsin; 3) South: Alabama, Arkansas, District of Columbia, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia; 4) West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, Wyoming. ↩
• Maine, North Dakota and Vermont had sample sizes too small to provide reliable estimates. ↩
• This section discusses moves, rather than people who moved; the six combined years of data likely include multiple moves by some people. ↩

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Protein domains of low sequence complexity—dark matter of the proteome

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In this study, Yoon et al. discover that Werner syndrome protein (WRN) and WRN-interacting protein 1 (WRNIP1) assemble with Rev1 and Y family DNA polymerases (Polη, Polι, or Polκ) upon DNA damage to facilitate error-free translesion DNA synthesis. This role requires WRN's exonuclease function to afford higher fidelity during replication through DNA lesions, which is an essential mechanism that protects against genomic instability.

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Published on 16.4.2024 in Vol 26 (2024)

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Authors of this article:

Original Paper

• Satoshi Nishioka 1 , PhD   ; 
• Satoshi Watabe 1 , BSc   ; 
• Yuki Yanagisawa 1 , PhD   ; 
• Kyoko Sayama 1 , MSc   ; 
• Hayato Kizaki 1 , MSc   ; 
• Shungo Imai 1 , PhD   ; 
• Mitsuhiro Someya 2 , BSc   ; 
• Ryoo Taniguchi 2 , PhD   ; 
• Shuntaro Yada 3 , PhD   ; 
• Eiji Aramaki 3 , PhD   ; 
• Satoko Hori 1 , PhD  

1 Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan

2 Nakajima Pharmacy, Hokkaido, Japan

3 Nara Institute of Science and Technology, Nara, Japan

Corresponding Author:

Satoko Hori, PhD

Division of Drug Informatics

Keio University Faculty of Pharmacy

1-5-30 Shibakoen

Tokyo, 105-8512

Phone: 81 3 5400 2650

Email: [email protected]

Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients.

Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed.

Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs.

Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives.

Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work.

Introduction

Increasing numbers of people are expected to develop cancers in our aging society [ 1 - 3 ]. Thus, there is increasing interest in how to detect and manage the side effects of anticancer therapies in order to improve treatment regimens and patients’ quality of life [ 4 - 8 ]. The primary approaches for side effect management are “early signal detection and early intervention” [ 9 - 11 ]. Thus, more efficient approaches for this purpose are needed.

It has been recognized that patients’ voices concerning adverse events represent an important source of information. Several studies have indicated that the number, severity, and time of occurrence of adverse events might be underevaluated by physicians [ 12 - 15 ]. Thus, patient-reported outcomes (PROs) have recently received more attention in the drug evaluation process, reflecting patients’ real voices. Various kinds of PRO measures have been developed and investigated in different disease populations [ 16 , 17 ]. Health care authorities have also encouraged the pharmaceutical industry to use PROs for drug evaluation [ 18 , 19 ], and it is becoming more common to take PRO assessment results into consideration for drug marketing approval [ 20 , 21 ]. Similar trends can be seen in the clinical management of individual patients. Thus, health care professionals have an interest in understanding how to appropriately gather patients’ concerns in order to improve safety management and clinical decisions [ 22 - 24 ].

The applications of deep learning for natural language processing have expanded dramatically in recent years [ 25 ]. Since the development of a high-performance deep learning model in 2018 [ 26 ], attempts to apply cutting-edge deep learning models to various kinds of patient-generated text data for the evaluation of safety events or the analysis of unscalable subjective information from patients have been accelerating [ 27 - 31 ]. Most studies have been conducted to use patients’ narrative data for pharmacovigilance [ 27 , 32 - 35 ], while few have been aimed at improvement of real-time safety monitoring for individual patients. In addition, there have been some studies on adverse event severity grading based on health care records [ 36 - 39 ], but none has yet aimed to extract clinically important adverse event signals that require medical intervention from patients’ narratives. It is important to know whether deep learning models could contribute to the detection of such important adverse event signals from concern texts generated by individual patients.

To address this question, we have developed deep learning models to detect adverse event signals from individual patients with cancer based on patients’ blog articles in online communities, following other types of natural language processing–related previous work [ 40 , 41 ]. One deep learning model focused on the specific symptom of hand-foot syndrome (HFS), which is one of the typical side effects of anticancer treatments [ 42 ], and another focused on a broad range of adverse events that impact patients’ activities of daily living [ 43 ]. We showed that our models can provide good performance scores in targeting adverse event signals. However, the evaluation relied on patients’ narratives from the patients’ blog data used for deep learning model training, so further evaluation is needed to ensure the validity and applicability of the models to other texts regarding patients’ concerns. In addition, the blog data source did not contain medical information, so it was not feasible to assess whether the models could contribute to the extraction of clinically important adverse event signals.

To address these challenges, we focused on pharmaceutical care records written by pharmacists at community pharmacies. The gold standard format for pharmaceutical care records in Japan is the SOAP (subjective, objective, assessment, plan)-based document that follows the “problem-oriented system” concept proposed by Weed [ 44 ] in 1968. Pharmacists track patients’ subjective concerns in the S column, provide objective information or observations in the O column, give their assessment from the pharmacist perspective in the A column, and suggest a plan for moving forward in the P column [ 45 , 46 ]. We considered that SOAP-based pharmaceutical care records could be a unique data source suitable for further evaluation of our deep learning models because they contain both patients’ concerns and professional health care records by pharmacists, including the medication prescription history with time stamps. Therefore, this study was designed to assess whether our deep learning models could extract clinically important adverse event signals that require intervention by medical professionals from these records. We also aimed to evaluate the characteristics of the models when applied to patients’ subjective information noted in the pharmaceutical care records, as there have been only a few studies on the application of deep learning models to patients’ concerns recorded during pharmacists’ daily work [ 47 - 49 ].

Here, we report the results of applying our deep learning models to patients’ concern text data in pharmaceutical care records, focusing on patients receiving anticancer treatment.

Data Source

The original data source was 2,276,494 pharmaceutical care records for 303,179 patients, created from April 2020 to December 2021 at community pharmacies belonging to the Nakajima Pharmacy Group in Japan [ 50 ]. To focus on patients with cancer, records of patients with at least 1 prescription for an anticancer drug were retrieved by sorting individual drug codes (YJ codes) used in Japan (YJ codes starting with 42 refer to anticancer drugs). Records in the S column (ie, S records) were collected from the patients with cancer as the text data of patients’ concerns for deep learning model analysis.

Deep Learning Models

The deep learning models used for this research were those that we constructed based on patients’ narratives in blog articles posted in an online community and that showed the best performance score in each task in our previous work (ie, a Bidirectional Encoder Representations From Transformers [BERT]–based model for HFS signal extraction [ 42 ] and a T5-based model for adverse event signal extraction [ 43 ]). BERT [ 26 ] and T5 [ 51 ] both belong to a type of deep learning model that has recently shown high performance in several studies [ 29 , 52 ]. Hereafter, we refer to the deep learning model for HFS signals as the HFS model, the model for any adverse event signals as All AE (ie, all or any adverse events) model, and the model for adverse event signals limited to patients’ activities of daily living as the AE-L (adverse events limiting patients’ daily lives) model. It was also confirmed that these deep learning models showed similar or higher performance scores for the HFS, All AE, or AE-L identification tasks using 1000 S records randomly extracted from the data source of this study compared to the values obtained in our previous work [ 42 , 43 ] (the performance scores of sentence-level tasks from our previous work are comparable, as the mean number of words in the sentences in the data source in our previous work was 32.7 [SD 33.9], which is close to that of the S records used in this study, 38.8 [SD 29.4]). The method and results of the performance-level check are described in detail in Multimedia Appendix 1 [ 42 , 43 ]. We applied the deep learning models to all text data in this study without any adjustment in setting parameters from those used in constructing them based on patient-authored texts in our previous work [ 42 , 43 ].

Evaluation of Extracted S Records by the Deep Learning Models

In this study, we focused on the evaluation of S records that our deep learning models extracted as HFS or AE-L positive. Each positive S record was assessed as if it was a true adverse event signal, a sort of adverse event symptom, whether or not an intervention was made by health care professionals. We also investigated the kind of anticancer treatment prescription in connection with each adverse event signal identified in S records.

To assess whether an extracted positive S record was a true adverse event signal, we used the same annotation guidelines as in our previous work [ 43 ]. In brief, each S record was treated as an “adverse event signal” if any untoward medical occurrence happened to the patient, regardless of the cause. For the AE-L model only, if a positive S record was confirmed as an adverse event signal, it was further categorized into 1 or more of the following adverse event symptoms: “fatigue,” “nausea,” “vomiting,” “diarrhea,” “constipation,” “appetite loss,” “pain or numbness,” “rash or itchy,” “hair loss,” “menstrual irregularity,” “fever,” “taste disorder,” “dizziness,” “sleep disorder,” “edema,” or “others.”

For the assessment of interventions by health care professionals and anticancer treatment prescriptions, information from the O, A, and P columns and drug prescription history in the data source were investigated for the extracted positive S records. The interventions by health care professionals were categorized in any of the following: “adding symptomatic treatment for the adverse event signal,” “dose reduction or discontinuation of causative anticancer treatment,” “consultation with physician,” “others,” or “no intervention (ie, just following up the adverse event signal).” The actions categorized in “others” were further evaluated individually. For this assessment, we also randomly extracted 200 S records and evaluated them in the same way for comparison with the results from the deep learning model. Prescription history of anticancer treatment was analyzed by primary category of mechanism of action (MoA) with subcategories if applicable (eg, target molecule for kinase inhibitors).

Applicability Check to Other Text Data Including Patients’ Concerns

To check the applicability of our deep learning models to data from a different source, interview transcripts from patients with cancer were also evaluated. The interview transcripts were created by the Database of Individual Patient Experiences-Japan (DIPEx-Japan) [ 53 ]. DIPEx-Japan divides the interview transcripts into sections for each topic, such as “onset of disease” and “treatment,” and posts the processed texts on its website. Processing is conducted by accredited researchers based on qualitative research methods established by the University of Oxford [ 54 ]. In this study, interview text data created from interviews with 52 patients with breast cancer conducted from January 2008 to October 2018 were used to assess whether our deep learning models can extract adverse event signals from this source. In total, 508 interview transcripts were included with the approval of DIPEx-Japan.

Ethical Considerations

This study was conducted with anonymized data following approval by the ethics committee of the Keio University Faculty of Pharmacy (210914-1 and 230217-1) and in accordance with relevant guidelines and regulations and the Declaration of Helsinki. Informed consent specific to this study was waived due to the retrospective observational design of the study with the approval of the ethics committee of the Keio University Faculty of Pharmacy. To respect the will of each individual stakeholder, however, we provided patients and pharmacists of the pharmacy group with an opportunity to refuse the sharing of their pharmaceutical care records by posting an overview of this study at each pharmacy store or on their web page regarding the analysis using pharmaceutical care records. Interview transcripts from DIPEx-Japan were provided through a data sharing arrangement for using narrative data for research and education. Consent for interview transcription and its sharing from DIPEx-Japan was obtained from the participants when the interviews were recorded.

From the original data source of 2,180,902 pharmaceutical care records for 291,150 patients, S records written by pharmacists for patients with a history of at least 1 prescription of an anticancer drug were extracted. This yielded 30,784 S records for 2479 patients with cancer ( Table 1 ). The mean and median number of words in the S records were 38.8 (SD 29.4) and 32 (IQR 20-50), respectively. We applied our deep learning models, HFS, All AE, and AE-L, to these 30,784 S records for the evaluation of the deep learning models for adverse event signal detection.

For interview transcripts created by DIPEx-Japan, the mean and median number of words were 428.9 (SD 160.9) and 416 (IQR 308-526), respectively, in the 508 transcripts for 52 patients with breast cancer.

a SOAP: subjective, objective, assessment, plan.

b S: subjective.

Application of the HFS Model

First, we applied the HFS model to the S records for patients with cancer. The BERT-based model was used for this research as it showed the best performance score in our previous work [ 42 ].

S Records Extracted as HFS Positive

The S records extracted as HFS positive by the HFS model ( Table 2 ) amounted to 167 (0.5%) records for 119 (4.8%) patients. A majority of the patients had 1 HFS-positive record in their S records (n=91, 76.5%), while 2 patients had as many as 6 (1.7%) HFS-positive records. When we examined whether the extracted S records were true adverse event signals or not, 152 records were confirmed to be adverse event signals, while the other 15 records were false-positives. All the false-positive S records were descriptions about the absence of symptoms or confirmation of improving condition (eg, “no diarrhea, mouth ulcers, or limb pain so far” or “the skin on the soles of my feet has calmed down a lot with this ointment”). Some examples of S records that were predicted as HFS positive by the model are shown in Table S1 in Multimedia Appendix 2 .

The same examination was conducted with interview transcripts from DIPEx-Japan. Only 1 (0.2%) transcript was extracted as HFS positive by the HFS model, and it was a true adverse event signal (100%). The actual transcript extracted as HFS positive is shown in Table S2 in Multimedia Appendix 2 .

a S: subjective.

b HFS: hand-foot syndrome.

c All false-positive S records were denial of symptoms or confirmation of improving condition.

Interventions by Health Care Professionals

The 167 S records extracted as HFS positive as well as 200 randomly selected records were checked for interventions by health care professionals ( Figure 1 ). The proportion showing any action by health care professionals was 64.1% for 167 HFS-positive S records compared to 13% for the 200 random S records. Among the actions taken for HFS positives, “adding symptomatic treatment” was the most common, accounting for around half (n=79, 47.3%), followed by “other” (n=18, 10.8%). Most “other” actions were educational guidance from pharmacists, such as instructions on moisturizing, nail care, or application of ointment and advice on daily living (eg, “avoid tight socks”).

Anticancer Drugs Prescribed

The types of anticancer drugs prescribed for HFS-positive patients are summarized based on the prescription histories in Table 3 . For the 152 adverse event signals identified by the HFS model in the previous section, the most common MoA class of anticancer drugs used for the patients was antimetabolite (n=62, 40.8%), specifically fluoropyrimidines (n=59, 38.8%). Kinase inhibitors were next (n=49, 32.2%), with epidermal growth factor receptor (EGFR) inhibitors and multikinase inhibitors as major subgroups (n=28, 18.4% and n=14, 9.2%, respectively). The third and fourth most common MoAs were aromatase inhibitors (n=24, 15.8%) and antiandrogen or estrogen drugs (n=7, 4.6% each) for hormone therapy.

a EGFR: epidermal growth factor receptor.

b VEGF: vascular endothelial growth factor.

c HER2: human epidermal growth factor receptor-2.

d CDK4/6: cyclin-dependent kinase 4/6.

Application of the All AE or AE-L model

The All AE and AE-L models were also applied to the same S records for patients with cancer. The T5-based model was used for this research as it gave the best performance score in our previous work [ 43 ].

S Records Extracted as All AE or AE-L positive

The numbers of S records extracted as positive were 7604 (24.7%) for 1797 patients and 196 (0.6%) for 142 patients for All AE and AE-L, respectively. In the case of All AE, patients tended to have multiple adverse event positives in their S records (n=1315, 73.2% of patients had at least 2 positives). In the case of AE-L, most patients had only 1 AE-L positive (n=104, 73.2%), and the largest number of AE-L positives for 1 patient was 4 (2.8%; Table 4 ).

We focused on AE-L evaluation due to its greater importance from a medical viewpoint and lower workload for manual assessment, considering the number of positive S records. Of the 197 AE-L–positive S records, it was confirmed that 157 (80.1%) records accurately extracted adverse event signals, while 39 (19.9%) records were false-positives that did not include any adverse event signals ( Table 4 ). The contents of the 39 false-positives were all descriptions about the absence of symptoms or confirmation of improving condition, showing a similar tendency to the HFS false-positives (eg, “The diarrhea has calmed down so far. Symptoms in hands and feet are currently fine” and “No symptoms for the following: upset in stomach, diarrhea, nausea, abdominal pain, abdominal pain or stomach cramps, constipation”). Examples of S records that were predicted as AE-L positive are shown in Table S3 in Multimedia Appendix 2 .

The deep learning models were also applied to interview transcripts from DIPEx-Japan in the same manner. The deep learning models identified 84 (16.5%) and 18 (3.5%) transcripts as All AE or AE-L positive, respectively. Of the 84 All AE–positive transcripts, 73 (86.9%) were true adverse event signals. The false-positives of All AE (n=11, 13.1%) were categorized into any of the following 3 types: explanations about the disease or its prognosis, stories when their cancer was discovered, or emotional changes that did not include clear adverse event mentions. With regard to AE-L, all the 18 (100%) positives were true adverse event signals (Table S4 in Multimedia Appendix 2 ). Examples of actual transcripts extracted as All AE or AE-L positive are shown in Table S5 in Multimedia Appendix 2 .

b All AE: all (or any of) adverse event.

c AE-L: adverse events limiting patients’ daily lives.

d All false-positive S records were denial of symptoms or confirmation of improving condition.

Whether or not interventions were made by health care professionals was investigated for the 196 AE-L–positive S records. As in the HFS model evaluation, data from 200 randomly selected S records were used for comparison ( Figure 2 ). In total, 91 (46.4%) records in the 196 AE-L–positive records were accompanied by an intervention, while the corresponding figure in the 200 random records was 26 (13%) records. The most common action in response to adverse event signals identified by the AE-L model was “adding symptomatic treatment” (n=71, 36.2%), followed by “other” (n=11, 5.6%). “Other” included educational guidance from pharmacists, inquiries from pharmacists to physicians, or recommendations for patients to visit a doctor.

The types of anticancer drugs prescribed for patients with adverse event signals identified by the AE-L model were summarized based on the prescription histories ( Table 5 ). In connection with the 157 adverse event signals, the most common MoA of the prescribed anticancer drug was antimetabolite (n=62, 39.5%) and fluoropyrimidine (n=53, 33.8%), which accounted for the majority. Kinase inhibitor (n=31, 19.7%) was the next largest category with multikinase inhibitor (n=14, 8.9%) as the major subgroup. These were followed by antiandrogen (n=27, 17.2%), antiestrogen (n=10, 6.4%), and aromatase inhibitor (n=10, 6.4%) for hormone therapy.

b JAK: janus kinase.

c VEGF: vascular endothelial growth factor.

d BTK: bruton tyrosine kinase.

e FLT3: FMS-like tyrosine kinase-3.

f PARP: poly-ADP ribose polymerase.

g CDK4/6: cyclin-dependent kinase 4/6.

h CD20: cluster of differentiation 20.

Adverse Event Symptoms

For the 157 adverse event signals identified by the AE-L model, the symptoms were categorized according to the predefined guideline in our previous work [ 43 ]. “Pain or numbness” (n=57, 36.3%) accounted for the largest proportion followed by “fever” (n=46, 29.3%) and “nausea” (n=40, 25.5%; Table 6 ). Symptoms classified as “others” included chills, tinnitus, running tears, dry or peeling skin, and frequent urination. When comparing the proportion of the symptoms associated with or without interventions by health care professionals, a trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). On the other hand, a smaller proportion was observed in “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes).

This study was designed to evaluate our deep learning models, previously constructed based on patient-authored texts posted in an online community, by applying them to pharmaceutical care records that contain both patients’ subjective concerns and medical information created by pharmacists. Based on the results, we discuss whether these deep learning models can extract clinically important adverse event signals that require medical intervention, and what characteristics they show when applied to data on patients’ concerns in pharmaceutical care records.

Performance for Adverse Event Signal Extraction

The first requirement for the deep learning models is to extract adverse event signals from patients’ narratives precisely. In this study, we evaluated the proportion of true adverse event signals in positive S records extracted by the HFS or AE-L model. True adverse event signals amounted to 152 (91%) and 157 (80.1%) for the HFS and AE-L models, respectively ( Tables 2 and 4 ). Given that the proportion of true adverse event signals in 200 randomly extracted S records without deep learning models was 54 (27%; categories other than “no adverse event” in Figures 1 and 2 ), the HFS and AE-L models were able to concentrate S records with adverse event mentions. Although 15 (9%) for the HFS model and 39 (19.9%) for the AE-L model were false-positives, it was confirmed all of the false-positive records described a lack of symptoms or confirmation of improving condition. We considered that such false-positives are due to the unique feature of pharmaceutical care records, where pharmacists might proactively interview patients about potential side effects of their medications. As the data set of blog articles we used to construct the deep learning models included few such cases (especially comments on lack of symptoms), our models seemed unable to exclude them correctly. Even though we confirmed that the proportion of true “adverse event” signals extracted from the S records by the HFS or AE-L model was more than 80%, the performance scores to extract true “HFS” or “AE-L” signals were not so high based on the performance check using 1000 randomly extracted S records ( F 1 -scores were 0.50 and 0.22 for true HFS and AE-L signals, respectively; Table S1 in Multimedia Appendix 1 ). It is considered that the performance to extract true HFS and AE-L signals was relatively low due to the short length of texts in the S records, providing less context to judge the impact on patients’ daily lives, especially for the AE-L model (the mean word number of the S records was 38.8 [SD 29.4; Table 1 ], similar to the sentence-level tasks in our previous work [ 42 , 43 ]). However, we consider a true adverse event signal proportion of more than 80% in this study represents a promising outcome, as this is the first attempt to apply our deep learning models to a different source of patients’ concern data, and the extracted positive cases would be worthy of evaluation by a medical professional, as the potential adverse events could be caused by drugs taken by the patients.

When the deep learning models were applied to DIPEx-Japan interview transcripts, including patients’ concerns, the proportion of true adverse event signals was also more than 80% (for All AE: n=73, 86.9% and for HFS and AE-L: n=18, 100%). The difference in the results between pharmaceutical care S records and DIPEx-Japan interview transcripts was the features of false-positives, descriptions about lack of symptoms or confirmation of improving condition in S records versus explanations about disease or its prognosis, stories about when their cancer was discovered, or emotional changes in interview transcripts. This is considered due to the difference in the nature of the data source; the pharmaceutical care records were generated in a real-time manner by pharmacists through their daily work, where adverse event signals are proactively monitored, while the interview transcripts were purely based on patients’ retrospective memories. Our deep learning models were able to extract true adverse event signals with an accuracy of more than 80% from both text data sources in spite of the difference in their nature. When looking at future implementation of the deep learning models in society (discussed in the Potential for Deep Learning Model Implementation in Society section), it may be desirable to further adjust deep learning models to reduce false-positives depending upon the features of the data source.

Identification of Important Adverse Events Requiring Medical Intervention

To assess whether the models could extract clinically important adverse event signals, we investigated interventions by health care professionals connected with the adverse event signals that are identified by our deep learning models. In the 200 randomly extracted S records, only 26 (13%) consisted of adverse event signals, leading to any intervention by health care professionals. On the other hand, the proportion of signals associated with interventions was increased to 107 (64.1%) and 91 (46.4%) in the S records extracted as positive by the HFS and AE-L models, respectively ( Figures 1 and 2 ). These results suggest that both deep learning models can screen clinically important adverse event signals that require intervention from health care professionals. The performance level in screening adverse event signals requiring medical intervention was higher in the HFS model than in the AE-L model (n=107, 64.1% vs n=91, 46.4%; Figures 1 and 2 ). Since the target events were specific and adverse event signals of HFS were narrowly defined, which is one of the typical side effects of some anticancer drugs, we consider that health care providers paid special attention to HFS-related signals and took action proactively. In both deep learning models, similar trends were observed in actions taken by health care professionals in response to extracted adverse event signals; common actions were attempts to manage adverse event symptoms by symptomatic treatment or other mild interventions, including educational guidance from pharmacists or recommendations for patients to visit a doctor. More direct interventions focused on the causative drugs (ie, “dose reduction or discontinuation of anticancer treatment”) amounted to less than 5%; 7 (4.2%) for the HFS model and 6 (3.1%) for the AE-L model ( Figures 1 and 2 ). Thus, it appears that our deep learning models can contribute to screening mild to moderate adverse event signals that require preventive actions such as symptomatic treatments or professional advice from health care providers, especially for patients with less sensitivity to adverse event signals or who have few opportunities to visit clinics and pharmacies.

Ability to Catch Real Side Effect Signals of Anticancer Drugs

Based on the drug prescription history associated with S records extracted as HFS or AE-L positive, the type and duration of anticancer drugs taken by patients experiencing the adverse event signals were investigated. For the HFS model, the most common MoA of anticancer drug was antimetabolite (fluoropyrimidine: n=59, 38.8%), followed by kinase inhibitors (n=49, 32.2%, of which EGFR inhibitors and multikinase inhibitors accounted for n=28, 18.4% and n=14, 9.2%, respectively) and aromatase inhibitors (n=24, 15.8%; Table 3 ). It is known that fluoropyrimidine and multikinase inhibitors are typical HFS-inducing drugs [ 55 - 58 ], suggesting that the HFS model accurately extracted HFS side effect signals derived from these drugs. Note that symptoms such as acneiform rash, xerosis, eczema, paronychia, changes in the nails, arthralgia, or stiffness of limb joints, which are common side effects of EGFR inhibitors or aromatase inhibitors [ 59 , 60 ], might be extracted as closely related expressions to those of HFS signals. When looking at the MoA of anticancer drugs for patients with adverse event signals identified by the AE-L model, antimetabolite (fluoropyrimidine) was the most common one (n=53, 33.8%), as in the case of those identified by the HFS model, followed by kinase inhibitors (n=31, 19.7%) and antiandrogens (n=27, 17.2%; Table 5 ). Since the AE-L model targets a broad range of adverse event symptoms, it is difficult to rationalize the relationship between the adverse event signals and types of anticancer drugs. However, the type of anticancer drugs would presumably closely correspond to the standard treatments of the cancer types of the patients. Based on the prescribed anticancer drugs, we can infer that a large percentage of the patients had breast or lung cancer, indicating that our study results were based on data from such a population. Thus, a possible direction for the expansion of this research would be adjusting the deep learning models by additional training with expressions for typical side effects associated with standard treatments of other cancer types. To interpret these results correctly, it should be noted that we could not investigate anticancer treatments conducted outside of the pharmacies (eg, the time-course relationship with intravenously administered drugs would be missed, as the administration will be done at hospitals). To further evaluate how useful this model is in side effect signal monitoring for patients with cancer, comprehensive medical information for the eligible patients would be required.

Suitability of the Deep Learning Models for Specific Adverse Event Symptoms

Among the adverse event signals identified by the AE-L model, the type of symptom was categorized according to a predefined annotation guideline that we previously developed [ 43 ]. The most frequently recorded adverse event signals identified by the AE-L model were “pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%; Table 6 ). Since the pharmaceutical care records had information about interventions by health care professionals, the frequency of the presence or absence of the interventions for each symptom was examined. A trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). There seem to be 2 possible explanations for this: these symptoms are of high importance and require early medical intervention or effective symptomatic treatments are available for these symptoms in clinical practice so that medical intervention is an easy option. On the other hand, a trend for a smaller proportion of adverse event signals to result in interventions was observed for “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes). The reason for this may be the lack of effective symptomatic treatments or the difficulty of judging whether the severity of these symptoms justifies medical intervention by health care providers. In either case, there may be room for improvement in the quality of medical care for these symptoms. We expect that our research will contribute to a quality improvement in safety monitoring in clinical practice by supporting adverse event signal detection in a cost-effective manner.

Potential for Deep Learning Model Implementation in Society

Although we evaluated our deep learning models using pharmaceutical care records in this study, the main target of future implementation of our deep learning models in society would be narrative texts that patients directly write to record their daily experiences. For example, the application of these deep learning models to electronic media where patients record their daily experiences in their lives with disease (eg, health care–related e-communities and disease diary applications) could enable information about adverse event signal onset that patients experience to be provided to health care providers in a timely manner. Adverse event signals can automatically be identified and shared with health care providers based on the concern texts that patients post to any platform. This system will have the advantage that health care providers can efficiently grasp safety-related events that patients experience outside of clinic visits so that they can conduct more focused or personalized interactions with patients at their clinic visits. However, consideration should be given to avoid an excessive burden on health care providers. For instance, limiting the sharing of adverse event signals to those of high severity or summarizing adverse event signals over a week rather than sharing each one in a real-time manner may be reasonable approaches for medical staff. We also need to think about how to encourage patients to record their daily experiences using electronic tools. Not only technical progress and support but also the establishment of an ecosystem where both patients and medical staff can feel benefit will be required. Prospective studies with deep learning models to follow up patients in the long term and evaluate outcomes will be needed. We primarily looked at patient-authored texts as targets of implementation, but our deep learning models may also be worth using medical data including patients’ subjective concerns, such as pharmaceutical care S records. As this study confirmed that our deep learning models are applicable to patients’ concern texts tracked by pharmacists, it should be possible to use them to analyze other “patient voice-like” medical text data that have not been actively investigated so far.

Limitations

First, the major limitation of this study was that we were not able to collect complete medical information of the patients. Although we designed this study to analyze patients’ concerns extracted by the deep learning models and their relationship with medical information contained in the pharmaceutical care records, some information could not be tracked (eg, missing history of medical interventions or anticancer treatment at hospitals as well as diagnosis of patients’ primary cancers). Second, there might be a data creation bias in S records for patients’ concerns by pharmacists. For example, symptoms that have little impact on intervention decisions might less likely be recorded by them. It should be also noted that the characteristics of S records may not be consistent at different community pharmacies.

Conclusions

Our deep learning models were able to screen clinically important adverse event signals that require intervention by health care professionals from patients’ concerns in pharmaceutical care records. Thus, these models have the potential to support real-time adverse event monitoring of individual patients taking anticancer treatments in an efficient manner. We also confirmed that these deep learning models constructed based on patient-authored texts could be applied to patients’ subjective information recorded by pharmacists through their daily work. Further research may help to expand the applicability of the deep learning models for implementation in society or for analysis of data on patients’ concerns accumulated in professional records at pharmacies or hospitals.

Acknowledgments

This work was supported by Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (KAKENHI; grant 21H03170) and Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (CREST; grant JPMJCR22N1), Japan. Mr Yuki Yokokawa and Ms Sakura Yokoyama at our laboratory advised SN about the structure of pharmaceutical care records. This study would not have been feasible without the high quality of pharmaceutical care records created by many individual pharmacists at Nakajima Pharmacy Group through their daily work.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

SN and SH designed the study. SN retrieved the subjective records of patients with cancer from the data source for the application of deep learning models and organized other data for subsequent evaluations. SN ran the deep learning models with the support of SW. SN, YY, and KS checked the adverse event signals for each subjective record that was extracted as positive by the models for hand-foot syndrome or adverse events limiting patients’ daily lives and evaluated the adverse event signal symptoms, details of interventions taken by health care professionals, and types of anticancer drugs prescribed for patients based on available data from the data source. HK and SI advised on the study concept and process. MS and RT provided pharmaceutical records at their community pharmacies along with advice on how to use and interpret them. SY and EA supervised the natural language processing research as specialists. SH supervised the study overall. SN drafted and finalized the paper. All authors reviewed and approved the paper.

Conflicts of Interest

SN is an employee of Daiichi Sankyo Co, Ltd. All other authors declare no conflicts of interest.

Performance evaluation of deep learning models.

Examples of S records and sample interview transcripts.

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Abbreviations

Edited by G Eysenbach; submitted 25.12.23; peer-reviewed by CY Wang, L Guo; comments to author 24.01.24; revised version received 14.02.24; accepted 09.03.24; published 16.04.24.

©Satoshi Nishioka, Satoshi Watabe, Yuki Yanagisawa, Kyoko Sayama, Hayato Kizaki, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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• Honors Thesis Option
• Advising for Psyc Majors
• Participant Pool
• Student Research Symposium

Table of Contents 

• 10:30 -11:45am: Poster Session 1 (setup 10:15-10:30am)
• 12:00 -1:00pm: Brief Talks (Lunch will be served)
• 1:15 - 2:30pm: Poster Session 2 (setup 1:00-1:15pm)

Poster Session 1: 10:30 - 11:45

Field placement, internship, practicum.

• Field Placement at Bluestone Elementary School Kate Becker-Mowery Advisor: Dr. Shayna Finn
• Field Placement at Community Counseling Center Lindsay Bernstein Advisor: Dr. Sarah Johnson

Undergraduate Research

• Effects of Cyclic Sighting on Social Connectedness Kevin Cottrell Advisor: Dr. Natalie Kerr Honors Thesis
• Attachment Style and Future Romantic Relationship Satisfaction: The Moderating Effects of Emotional Expressivity, Communication during Conflict, and Self-Sacrifice Abigail Young Advisor: Dr. David Szwedo Honors Thesis
• The Development of Self-Worth Throughout Adolescence Lindsay Bernstein Advisor: Dr. David Szwedo Honors Thesis
• Comedy in the Classroom: Research Proposal for an Investigation on the Effects of Comic Creation on Memory Yasmin Ohanian & Maggie Allen Advisor: Dr. Suzanne Baker
• Connections Between Family Relationships, Autonomy, and Substance Use Behaviors Lindsey Altizer Advisor: Dr. David Szwedo
• Are Students “Punitive” in Their Requests for Content Warnings? Emily Triplett Advisor: Dr. Jessica Salvatore
• Pluralistic Ignorance Regarding Hazing Activities Jacob Kalman & Reese Eelkema Advisor: Dr. Kevin Apple
• Beliefs and Behaviors of College Students that Identify as Superfans Jordyn Crane & Savannah Totty Advisor: Dr. Monica Reis-Bergan
• Happy Face Advantage in Children: Human and Pareidolic Faces Katie Fox, Rachael Johnson, & Katelyn Morgan Advisor: Dr. Krisztina Jakobsen
• Adolescent Interpersonal Competence and Adult Romantic Relationship Outcomes: The Moderating Role of Relationship Satisfaction Grace Kinsky Advisor: Dr. David Szwedo
• Adult Ratings of Faces, Face-Like Images, and Objects Margaret Nelson & Sophia Cundiff Advisor: Dr. Krisztina Jakobsen
• Add Confetti to the Fire: Conveying Object Emoji with Time Delay Responses Katevonni Sorlouangsana, Jessica Johnson, & Francis Powers, III Advisor: Dr. Sri Siddhi N. Upadhyay
• Social Appraisal and Student Attention Lyndsay Maguire Advisor: Dr. Claire Lyons
• The relationship among oral contraceptive use, sleep, and sexual behavior Sureyya Sanchez, Caitlyn Hewitt, Olivia Hornung, Kailee Corbett, Kieran Donohue, Cameron McKinney, & Alex Sheffield Advisor: Dr. Jeff Dyche
• Choice among Supplemental Readings Leads to Less Boredom and Better Learning David Morales Advisor: Dr. Ashton Trice

Brief Talks: 12:00 - 1:00 pm

Title: Relationships among Emotional Vulnerability, Social Support, and Flourishing in Emerging Adults Presenter: Hannah Dodson Advisor: Dr. Claire Lyons

Title: The Forgotten Variable: Is Experimenter Gender a Missing Factor in the Reproducibility Crisis?  Presenters: Amber Bennett, Braden Berger, Jack Nocé, and Abigail Sholes Advisor: Dr. Melanie Shoup-Knox

Exploring Meaning of Life in Suicide Awareness Campaigns: An Investigation of Authenticity in the Campaign Against Living Miserably (CALM's) "The Last Photo" Video Series"  Presenters: Paige D'Agostino and Teagan Davis Advisor: Dr. Lindsay Harvell-Bowman

Title: Crafting the Situation to Promote Social Connection among JMU Students Presenters: McKenna Mattingly, Maddie Roever, Liana Turturro, and Jana Donato Advisor: Dr. Natalie Kerr

Poster Session 2: 1:15 - 2:30 pm

• Field Placement Experience at Western State Hospital Caitlyn Hewitt Advisor: Dr. Shayna Finn
• Independent Study (PSYC 402) – Peer Tutoring in Abnormal Psychology​ Jessica Johnson, Courtney Farzan, Jessica Papandon, Emily Triplett, & Sam Carberry Advisor: Dr. JoAnne Brewster
• Musical Training’s Relation to Working Memory Capacity and Attentional Interference Liam Payne Advisor: Dr. Michael Hall Honors Thesis
• The Relationship Between Early Parent-Child Relationships and College Roommate Compatibility Rachael Stromberg Advisor: Dr. Claire Lyons Honors Thesis
• Engaging in Politics: Early Exploration between Mindfulness and Political Engagement Tanner Sykhammountry Advisor: Dr. Ben Blankenship Honors Thesis
• Validation of the Avoid, Redirect, or Confront (ARC) Scale Penny Naden, Rohan Saini, Urmi Chatterjee, & Amy Spears Advisor: Dr. Kala Melchiori
• Happiness 101: Studying the Well-Being of JMU Students Sara Buie, Zach Baron, Sammi Blecke, Emily Bousquet, Ziah Buffington, Kaylin Carsey, Sean Cattie, Andrew Gray, Alex Hatgimisios, Shelby Heath, Kaleigh Higgins, Savannah Hughes, Ruby Loeper-Viti, Hana Mahyoub, Ryan Monroe, Caroline Morel, Heather Narvaez, Kyla Powers, Kayla Ransome, Abby Reinhardt, Bryan Rodeheaver, Khalil Turner, and Will Whited. Advisor: Dr. Kenn Barron
• Influencer Aspirations: Extraversion, Narcissism, and Social Media Soleil Goodrich Advisor: Dr. Monica Reis-Bergan
• A Quantitative Investigation of Relational Cognitive Distortions Among the Suicidal with Traditional Terror Management Measures Teagan Davis, Sophia Cundiff, Paige D'Agostino, Ethan Raymond, Brielle Lampf, Lizzie Bryan, Elsa Herries, Grace Kinsky, Jack Cignetti, Erin Fuller, Emily Nicholson , Anna Robinson, & Eliza Snipes Advisor: Dr. Lindsey Harvell-Bowman
• Existential Flourishing: Exploring Relational Cognition as a Mechanism to Produce More Therapeutic Experiences Teagan Davis, Sophia Cundiff, Paige D'Agostino, Ethan Raymond, Brielle Lampf, Lizzie Bryan, Elsa Herries, Grace Kinsky, Jack Cignetti, Erin Fuller, Emily Nicholson , Anna Robinson, & Eliza Snipes Advisor: Dr. Lindsey Harvell-Bowman
• How Accurate is Deception Detection in Capital Murder 911 Calls? Alexandra Sheffield Advisor: Dr. Kethera Moore
• Analysis of 911 Calls Alexandra Sheffield Advisor: Dr. Kethera Moore
• What Helps and Hinders Student Transitions to JMU Nenah Conners, Savannah Hoover, Teagan Lunn, Andrew Goldfeder, Lyndsay Maguire, Rachel Mitchem, McKenna Murphy, & Yasasvi Paleti Advisor: Dr. Kenn Barron
• Wear and Compare: The effects of Body Neutral and Positive Language Amanda Ball Advisor: Dr. Monica Reis-Bergan
• Racial Differences with the ARC Scale Morgan Melchor, Chubi Abu, & Emma Roberson Advisor: Dr. Kala Melchiori
• Can prepubescent children detect ovulation cues? Rebecca Fleetwood, Annalise Kallman, Jordyn Crane, Lindsay Moody, & Anna Pierce Advisors: Dr. Melanie Shoup-Knox and Dr. Krisztina Jakobsen

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Chapter 4: Economic Growth for Every Generation

On this page:, 4.1 boosting research, innovation, and productivity, 4.2 attracting investment for a net-zero economy, 4.3 growing businesses to create more jobs.

• 4.4 A Strong Workforce for a Strong Economy

To ensure every Canadian succeeds in the 21 st century, we must grow our economy to be more innovative and productive. One where every Canadian can reach their full potential, where every entrepreneur has the tools they need to grow their business, and where hard work pays off. Building the economy of the future is about creating jobs: jobs in the knowledge economy, jobs in manufacturing, jobs in mining and forestry, jobs in the trades, jobs in clean energy, and jobs across the economy, in all regions of the country.

To do this, the government's economic plan is investing in the technologies, incentives, and supports critical to increasing productivity, fostering innovation, and attracting more private investment to Canada. This is how we'll build an economy that unlocks new pathways for every generation to earn their fair share.

The government is targeting investments to make sure Canada continues to lead in the economy of the future, and these are already generating stronger growth and meaningful new job opportunities for Canadians. New jobs—from construction to manufacturing to engineering—in clean technology, in clean energy, and in innovation, are just the start. All of this, helping to attract further investment to create more opportunities, will raise Canada's productivity and competitiveness. This will create more good jobs, and in turn, raise the living standards of all Canadians.

We are at a pivotal moment where we can choose to renew and redouble our investments in the economy of the future, to build an economy that is more productive and more competitive—or risk leaving an entire generation behind. We will not make that mistake. We owe it to our businesses, to our innovators, and most of all, to the upcoming generations of workers, to make sure that the Canadian economy is positioned to thrive in a changing world.

Canada has the best-educated workforce in the world. We are making investments to ensure every generation of workers has the skills the job market, and the global economy, are looking for—and this will help us attract private investment to grow the economy (Chart 4.1). Building on our talented workforce, we are delivering, on a priority basis, our $93 billion suite of major economic investment tax credits to drive growth, secure the future of Canadian businesses in Canada, and create good jobs for generations to come.

In the first three quarters of 2023, Canada had the highest level of foreign direct investment (FDI) on a per capita basis among G7 countries, and ranked third globally in total FDI, after the U.S. and Brazil (Chart 4.2).

The Canadian economy is adding new, high-paying jobs, in high-growth sectors, like clean tech, clean electricity, and scientific research and development (Chart 4.4). Budget 2024 will continue this momentum by making strategic investments that create opportunities for workers today—driving productivity and economic growth for generations to come.

Impacts report

Find out more about the expected gender and diversity impacts for each measure in section 4.1 Boosting Research, Innovation, and Productivity

Canada's skilled hands and brilliant minds are our greatest resource. Capitalizing on their ideas, innovations, and hard work is an essential way to keep our place at the forefront of the world's advanced economies. Our world-class innovators, entrepreneurs, scientists, and researchers are solving the most pressing challenges of today, and their discoveries help launch the businesses of tomorrow.

Canadian researchers, entrepreneurs, and companies are the driving force of this progress—from scientific discovery to bringing new solutions to market. They also train and hire younger Canadians who will become the next generation of innovators. New investments to boost research and innovation, including enhancing support for graduate students and post-doctoral fellows, will ensure Canada remains a world leader in science and new technologies, like artificial intelligence.

By making strategic investments today in innovation and research, and supporting the recruitment and development of talent in Canada, we can ensure Canada is a world leader in new technologies for the next generation. In turn, this will drive innovation, growth, and productivity across the economy.

Key Ongoing Actions

• Supporting scientific discovery, developing Canadian research talent, and attracting top researchers from around the planet to make Canada their home base for their important work with more than $16 billion committed since 2016.
• Supporting critical emerging sectors, through initiatives like the Pan-Canadian Artificial Intelligence Strategy, the National Quantum Strategy, the Pan-Canadian Genomics Strategy, and the Biomanufacturing and Life Sciences Strategy.
• Nearly $2 billion to fuel Canada's Global Innovation Clusters to grow these innovation ecosystems, promote commercialization, support intellectual property creation and retention, and scale Canadian businesses.
• Investing $3.5 billion in the Sustainable Canadian Agricultural Partnership to strengthen the innovation, competitiveness, and resiliency of the agriculture and agri-food sector.
• Flowing up to $333 million over the next decade to support dairy sector investments in research, product and market development, and processing capacity for solids non-fat, thus increasing its competitiveness and productivity.

Strengthening Canada's AI Advantage

Canada's artificial intelligence (AI) ecosystem is among the best in the world. Since 2017, the government has invested over $2 billion towards AI in Canada. Fuelled by those investments, Canada is globally recognized for strong AI talent, research, and its AI sector.

Today, Canada's AI sector is ranked first in the world for growth of women in AI, and first in the G7 for year-over-year growth of AI talent. Every year since 2019, Canada has published the most AI-related papers, per capita, in the G7. Our AI firms are filing patents at three times the average rate in the G7, and they are attracting nearly a third of all venture capital in Canada. In 2022-23, there were over 140,000 actively engaged AI professionals in Canada, an increase of 29 per cent compared to the previous year. These are just a few of Canada's competitive advantages in AI and we are aiming even higher.

To secure Canada's AI advantage, the government has already:

• Established the first national AI strategy in the world through the Pan-Canadian Artificial Intelligence Strategy;
• Supported access to advanced computing capacity, including through the recent signing of a letter of intent with NVIDIA and a Memorandum of Understanding with the U.K. government; and,
• Scaled-up Canadian AI firms through the Strategic Innovation Fund and Global Innovation Clusters program.

AI is a transformative economic opportunity for Canada and the government is committed to doing more to support our world-class research community, launch Canadian AI businesses, and help them scale-up to meet the demands of the global economy. The processing capacity required by AI is accelerating a global push for the latest technology, for the latest computing infrastructure.

Currently, most compute capacity is located in other countries. Challenges accessing compute power slows down AI research and innovation, and also exposes Canadian firms to a reliance on privately-owned computing, outside of Canada. This comes with dependencies and security risks. And, it is a barrier holding back our AI firms and researchers.

We need to break those barriers to stay competitive in the global AI race and ensure workers benefit from the higher wages of AI transformations; we must secure Canada's AI advantage. We also need to ensure workers who fear their jobs may be negatively impacted by AI have the tools and skills training needed in a changing economy.

To secure Canada's AI advantage Budget 2024 announces a monumental increase in targeted AI support of $2.4 billion, including:

• $2 billion over five years, starting in 2024-25, to launch a new AI Compute Access Fund and Canadian AI Sovereign Compute Strategy, to help Canadian researchers, start-ups, and scale-up businesses access the computational power they need to compete and help catalyze the development of Canadian-owned and located AI infrastructure. 
• $200 million over five years, starting in 2024-25, to boost AI start-ups to bring new technologies to market, and accelerate AI adoption in critical sectors, such as agriculture, clean technology, health care, and manufacturing. This support will be delivered through Canada's Regional Development Agencies.
• $100 million over five years, starting in 2024-25, for the National Research Council's AI Assist Program to help Canadian small- and medium-sized businesses and innovators build and deploy new AI solutions, potentially in coordination with major firms, to increase productivity across the country.
• $50 million over four years, starting in 2025-26, to support workers who may be impacted by AI, such as creative industries. This support will be delivered through the Sectoral Workforce Solutions Program, which will provide new skills training for workers in potentially disrupted sectors and communities.

The government will engage with industry partners and research institutes to swiftly implement AI investment initiatives, fostering collaboration and innovation across sectors for accelerated technological advancement.

Safe and Responsible Use of AI

AI has tremendous economic potential, but as with all technology, it presents important considerations to ensure its safe development and implementation. Canada is a global leader in responsible AI and is supporting an AI ecosystem that promotes responsible use of technology. From development through to implementation and beyond, the government is taking action to protect Canadians from the potentially harmful impacts of AI.

The government is committed to guiding AI innovation in a positive direction, and to encouraging the responsible adoption of AI technologies by Canadians and Canadian businesses. To bolster efforts to ensure the responsible use of AI:

• Budget 2024 proposes to provide $50 million over five years, starting in 2024-25, to create an AI Safety Institute of Canada to ensure the safe development and deployment of AI. The AI Safety Institute will help Canada better understand and protect against the risks of advanced and generative AI systems. The government will engage with stakeholders and international partners with competitive AI policies to inform the final design and stand-up of the AI Safety Institute.
• Budget 2024 also proposes to provide $5.1 million in 2025-26 to equip the AI and Data Commissioner Office with the necessary resources to begin enforcing the proposed Artificial Intelligence and Data Act .
• Budget 2024 proposes $3.5 million over two years, starting in 2024-25, to advance Canada's leadership role with the Global Partnership on Artificial Intelligence, securing Canada's leadership on the global stage when it comes to advancing the responsible development, governance, and use of AI technologies internationally.

Using AI to Keep Canadians Safe

AI has shown incredible potential to toughen up security systems, including screening protocols for air cargo. Since 2012, Transport Canada has been testing innovative approaches to ensure that air cargo coming into Canada is safe, protecting against terrorist attacks. This included launching a pilot project to screen 10 to 15 per cent of air cargo bound for Canada and developing an artificial intelligence system for air cargo screening.

• Budget 2024 proposes to provide $6.7 million over five years, starting in 2024-25, to Transport Canada to establish the Pre-Load Air Cargo Targeting Program to screen 100 per cent of air cargo bound for Canada. This program, powered by cutting-edge artificial intelligence, will increase security and efficiency, and align Canada's air security regime with those of its international partners.

Incentivizing More Innovation and Productivity

Businesses that invest in cutting-edge technologies are a key driver of Canada's economic growth. When businesses make investments in technology—from developing new patents to implementing new IT systems—it helps ensure Canadian workers put their skills and knowledge to use, improves workplaces, and maximizes our workers' potential and Canada's economic growth.

The government wants to encourage Canadian businesses to invest in the capital—both tangible and intangible—that will help them boost productivity and compete productively in the economy of tomorrow.

• To incentivize investment in innovation-enabling and productivity-enhancing assets, Budget 2024 proposes to allow businesses to immediately write off the full cost of investments in patents, data network infrastructure equipment, computers, and other data processing equipment. Eligible investments, as specified in the relevant capital cost allowance classes, must be acquired and put in use on or after Budget Day and before January 1, 2027. The cost of this measure is estimated at $725 million over five years, starting in 2024-25.

Boosting R&D and Intellectual Property Retention

Research and development (R&D) is a key driver of productivity and growth. Made-in-Canada innovations meaningfully increase our gross domestic product (GDP) per capita, create good-paying jobs, and secure Canada's position as a world-leading advanced economy.

To modernize and improve the Scientific Research and Experimental Development (SR&ED) tax incentives, the federal government launched consultations on January 31, 2024, to explore cost-neutral ways to enhance the program to better support innovative businesses and drive economic growth. In these consultations, which closed on April 15, 2024, the government asked Canadian researchers and innovators for ways to better deliver SR&ED support to small- and medium-sized Canadian businesses and enable the next generation of innovators to scale-up, create jobs, and grow the economy.

• Budget 2024 announces the government is launching a second phase of consultations on more specific policy parameters, to hear further views from businesses and industry on specific and technical reforms. This includes exploring how Canadian public companies could be made eligible for the enhanced credit. Further details on the consultation process will be released shortly on the Department of Finance Canada website.
• Budget 2024 proposes to provide $600 million over four years, starting in 2025-26, with $150 million per year ongoing for future enhancements to the SR&ED program. The second phase of consultations will inform how this funding could be targeted to boost research and innovation.

On January 31, 2024, the government also launched consultations on creating a patent box regime to encourage the development and retention of intellectual property in Canada. The patent box consultation closed on April 15, 2024. Submissions received through this process, which are still under review, will help inform future government decisions with respect to a patent box regime.

Enhancing Research Support

Since 2016, the federal government has committed more than $16 billion in research, including funding for the federal granting councils—the Natural Sciences and Engineering Research Council (NSERC), the Canadian Institutes of Health Research (CIHR), and the Social Sciences and Humanities Research Council (SSHRC).

This research support enables groundbreaking discoveries in areas such as climate change, health emergencies, artificial intelligence, and psychological health. This plays a critical role in solving the world's greatest challenges, those that will have impacts for generations.

Canada's granting councils already do excellent work within their areas of expertise, but more needs to be done to maximize their effect. The improvements we are making today, following extensive consultations including with the Advisory Panel on the Federal Research Support System, will strengthen and modernize Canada's federal research support.

• To increase core research grant funding and support Canadian researchers, Budget 2024 proposes to provide $1.8 billion over five years, starting in 2024-25, with $748.3 million per year ongoing to SSHRC, NSERC, and CIHR.
• To provide better coordination across the federally funded research ecosystem, Budget 2024 announces the government will create a new capstone research funding organization. The granting councils will continue to exist within this new organization, and continue supporting excellence in investigator-driven research, including linkages with the Health portfolio. This new organization and structure will also help to advance internationally collaborative, multi-disciplinary, and mission-driven research. The government is delivering on the Advisory Panel's observation that more coordination is needed to maximize the impact of federal research support across Canada's research ecosystem.
• To help guide research priorities moving forward, Budget 2024 also announces the government will create an advisory Council on Science and Innovation. This Council will be made up of leaders from the academic, industry, and not-for-profit sectors, and be responsible for a national science and innovation strategy to guide priority setting and increase the impact of these significant federal investments.
• Budget 2024 also proposes to provide a further $26.9 million over five years, starting in 2024-25, with $26.6 million in remaining amortization and $6.6 million ongoing, to the granting councils to establish an improved and harmonized grant management system.

The government will also work with other key players in the research funding system—the provinces, territories, and Canadian industry—to ensure stronger alignment, and greater co-funding to address important challenges, notably Canada's relatively low level of business R&D investment.

More details on these important modernization efforts will be announced in the 2024 Fall Economic Statement.

World-Leading Research Infrastructure

Modern, high-quality research facilities and infrastructure are essential for breakthroughs in Canadian research and science. These laboratories and research centres are where medical and other scientific breakthroughs are born, helping to solve real-world problems and create the economic opportunities of the future. World-leading research facilities will attract and train the next generation of scientific talent. That's why, since 2015, the federal government has made unprecedented investments in science and technology, at an average of $13.6 billion per year, compared to the average from 2009-10 to 2015-16 of just $10.8 billion per year. But we can't stop here.

To advance the next generation of cutting-edge research, Budget 2024 proposes major research and science infrastructure investments, including:

• $399.8 million over five years, starting in 2025-26, to support TRIUMF, Canada's sub-atomic physics research laboratory, located on the University of British Columbia's Vancouver campus. This investment will upgrade infrastructure at the world's largest cyclotron particle accelerator, positioning TRIUMF, and the partnering Canadian research universities, at the forefront of physics research and enabling new medical breakthroughs and treatments, from drug development to cancer therapy.
• $176 million over five years, starting in 2025‑26, to CANARIE, a national not-for-profit organization that manages Canada's ultra high-speed network to connect researchers, educators, and innovators, including through eduroam. With network speeds hundreds of times faster, and more secure, than conventional home and office networks, this investment will ensure this critical infrastructure can connect researchers across Canada's world-leading post-secondary institutions.
• $83.5 million over three years, starting in 2026-27 to extend support to Canadian Light Source in Saskatoon. Funding will continue the important work at the only facility of its kind in Canada. A synchrotron light source allows scientists and researchers to examine the microscopic nature of matter. This specialized infrastructure contributes to breakthroughs in areas ranging from climate-resistant crop development to green mining processes.
• $45.5 million over five years, starting in 2024-25, to support the Arthur B. McDonald Canadian Astroparticle Physics Research Institute, a network of universities and institutes that coordinate astroparticle physics expertise. Headquartered at Queen's University in Kingston, Ontario, the institute builds on the legacy of Dr. McDonald's 2015 Nobel Prize for his work on neutrino physics. These expert engineers, technicians, and scientists design, construct, and operate the experiments conducted in Canada's underground and underwater research infrastructure, where research into dark matter and other mysterious particles thrives. This supports innovation in areas like clean technology and medical imaging, and educates and inspires the next wave of Canadian talent.
• $30 million over three years, starting in 2024-25, to support the completion of the University of Saskatchewan's Centre for Pandemic Research at the Vaccine and Infectious Disease Organization in Saskatoon. This investment will enable the study of high-risk pathogens to support vaccine and therapeutic development, a key pillar in Canada's Biomanufacturing and Life Sciences Strategy. Of this amount, $3 million would be sourced from the existing resources of Prairies Economic Development Canada.

These new investments build on existing federal research support:

• The Strategic Science Fund, which announced the results of its first competition in December 2023, providing support to 24 third-party science and research organizations starting in 2024-25;
• Canada recently concluded negotiations to be an associate member of Horizon Europe, which would enable Canadians to access a broader range of research opportunities under the European program starting this year; and,
• The steady increase in federal funding for extramural and intramural science and technology by the government which was 44 per cent higher in 2023 relative to 2015.

Investing in Homegrown Research Talent

Canada's student and postgraduate researchers are tackling some of the world's biggest challenges. The solutions they come up with have the potential to make the world a better place and drive Canadian prosperity. They are the future Canadian academic and scientific excellence, who will create new innovative businesses, develop new ways to boost productivity, and create jobs as they scale-up companies—if they get the support they need.

To build a world-leading, innovative economy, and improve our productive capacity, the hard work of top talent must pay off; we must incentivize our top talent to stay here.

Federal support for master's, doctoral, and post-doctoral students and fellows has created new research opportunities for the next generation of scientific talent. Opportunities to conduct world-leading research are critical for growing our economy. In the knowledge economy, the global market for these ideas is highly competitive and we need to make sure talented people have the right incentives to do their groundbreaking research here in Canada.

• To foster the next generation of research talent, Budget 2024 proposes to provide $825 million over five years, starting in 2024-25, with $199.8 million per year ongoing, to increase the annual value of master's and doctoral student scholarships to $27,000 and $40,000, respectively, and post-doctoral fellowships to $70,000. This will also increase the number of research scholarships and fellowships provided, building to approximately 1,720 more graduate students or fellows benefiting each year. To make it easier for students and fellows to access support, the enhanced suite of scholarships and fellowship programs will be streamlined into one talent program.
• To support Indigenous researchers and their communities, Budget 2024 also proposes to provide $30 million over three years, starting in 2024-25, to support Indigenous participation in research, with $10 million each for First Nation, Métis, and Inuit partners.

Boosting Talent for Innovation

Advanced technology development is a highly competitive industry and there is a global race to attract talent and innovative businesses. Canada must compete to ensure our economy is at the forefront of global innovation.

To spur rapid growth in innovation across Canada's economy, the government is partnering with organizations whose mission it is to train the next generation of innovators. This will ensure innovative businesses have the talent they need to grow, create jobs at home, and drive Canada's economic growth.

• Budget 2024 announces the government's intention to work with Talent for Innovation Canada to develop a pilot initiative to build an exceptional research and development workforce in Canada. This industry-led pilot will focus on attracting, training, and deploying top talent across four key sectors: bio-manufacturing; clean technology; electric vehicle manufacturing; and microelectronics, including semiconductors.

Advancing Space Research and Exploration

Canada is a leader in cutting-edge innovation and technologies for space research and exploration. Our astronauts make great contributions to international space exploration missions. The government is investing in Canada's space research and exploration activities.

• Budget 2024 proposes to provide $8.6 million in 2024-25 to the Canadian Space Agency for the Lunar Exploration Accelerator Program to support Canada's world-class space industry and help accelerate the development of new technologies. This initiative empowers Canada to leverage space to solve everyday challenges, such as enhancing remote health care services and improving access to healthy food in remote communities, while also supporting Canada's human space flight program.
• Budget 2024 announces the establishment of a new whole-of-government approach to space exploration, technology development, and research. The new National Space Council will enable the level of collaboration required to secure Canada's future as a leader in the global space race, addressing cross-cutting issues that span commercial, civil, and defence domains. This will also enable the government to leverage Canada's space industrial base with its world-class capabilities, workforce, and track record of innovation and delivery.

Accelerating Clean Tech Intellectual Property Creation and Retention

Canadian clean technology companies are turning their ideas into the solutions that the world is looking for as it races towards net-zero. Encouraging these innovative companies to maintain operations in Canada and retain ownership of their intellectual property secures the future of their workforce in Canada, helping the clean economy to thrive in Canada.

As part of the government's National Intellectual Property Strategy, the not-for-profit organization Innovation Asset Collective launched the patent collective pilot program in 2020. This pilot program is helping innovative small- and medium-sized enterprises in the clean tech sector with the creation and retention of intellectual property.

• To ensure that small- and medium-sized clean tech businesses benefit from specialized intellectual property support to grow their businesses and leverage intellectual property, Budget 2024 proposes to provide $14.5 million over two years, starting in 2024-25, to Innovation, Science and Economic Development Canada for the Innovation Asset Collective.

Find out more about the expected gender and diversity impacts for each measure in section 4.2 Attracting Investment for a Net-Zero Economy

In the 21 st century, a competitive economy is a clean economy. There is no greater proof than the $2.4 trillion worth of investment made around the world, last year, in net-zero economies. Canada is at the forefront of the global race to attract investment and seize the opportunities of the clean economy, with the government announcing a net-zero economic plan that will invest over $160 billion. This includes an unprecedented suite of major economic investment tax credits, which will help attract investment through $93 billion in incentives by 2034-35.

All told, the government's investments will crowd in more private investment, securing Canadian leadership in clean electricity and innovation, creating economic growth and more good-paying jobs across the country.

Investors at home and around the world are taking notice of Canada's plan. In defiance of global economic headwinds, last year public markets and private equity capital flows into Canada's net-zero economy grew—reaching $14 billion in 2023, according to RBC. Proof that Canada's investments are working—driving new businesses to take shape, creating good jobs, and making sure that we have clean air and clean water for our kids, grandkids, and for generations to come.

Earlier this year, BloombergNEF ranked Canada's attractiveness to build electric vehicle (EV) battery supply chains first in the world, surpassing China which has held the top spot since the ranking began. From resource workers mining the critical minerals for car batteries, to union workers on auto assembly lines, to the truckers that get cars to dealerships, Canada's advantage in the supply chain is creating high-skilled, good-paying jobs across the country, for workers of all ages.

This first place ranking of Canada's EV supply chains is underpinned by our abundant clean energy, high labour standards, and rigorous standards for consultation and engagement with Indigenous communities. That's what Canada's major economic investment tax credits are doing—seizing Canada's full potential, and doing it right.

By 2050, clean energy GDP could grow fivefold—up to $500 billion, while keeping Canada on track to reach net-zero by 2050. Proof, once again, that good climate policy is good economic policy.

Helping innovative Canadian firms scale-up is essential to increasing the pace of economic growth in Canada. Already, the Cleantech Group's 2023 list of the 100 most innovative global clean technology companies featured 12 Canadian companies, the second highest number of any country, behind only the U.S. The government is investing in clean technology companies to ensure their full capabilities are unlocked.

Budget 2024 announces the next steps in the government's plan to attract even more investment to Canada to create good-paying jobs and accelerate the development and deployment of clean energy and clean technology.

• Carbon Capture, Utilization, and Storage investment tax credit;
• Clean Technology investment tax credit;
• Clean Hydrogen investment tax credit;
• Clean Technology Manufacturing investment tax credit; and
• Clean Electricity investment tax credit.
• Since the federal government launched the Canada Growth Fund last year, $1.34 billion of capital has been committed to a world-leading geothermal energy technology company, the world's first of its kind carbon contract for difference; and to clean tech entrepreneurs and innovators through a leading Canadian-based climate fund.
• Working with industry, provinces, and Indigenous partners to build an end-to-end electric vehicle battery supply chain, including by securing major investments in 2023.
• Building major clean electricity and clean growth infrastructure projects with investments of at least $20 billion from the Canada Infrastructure Bank.
• $3.8 billion for Canada's Critical Minerals Strategy, to secure our position as the world's supplier of choice for critical minerals and the clean technologies they enable.
• $3 billion to recapitalize the Smart Renewables and Electrification Pathways Program, which builds more clean, affordable, and reliable power, and to support innovation in electricity grids and spur more investments in Canadian offshore wind.

A New EV Supply Chain Investment Tax Credit

The automotive industry is undergoing a major transformation. As more and more electric vehicles are being produced worldwide, it is essential that Canada's automotive industry has the support it needs to retool its assembly lines and build new factories to seize the opportunities of the global switch to electric vehicles. With our world-class natural resource base, talented workforce, and attractive investment climate, Canada will be an electric vehicle supply chain hub for all steps along the manufacturing process. This is an opportunity for Canada to secure its position today at the forefront of this growing global supply chain and secure high-quality jobs for Canadian workers for a generation to come.

Businesses that manufacture electric vehicles and their precursors would already be able to claim the 30 per cent Clean Technology Manufacturing investment tax credit on the cost of their investments in new machinery and equipment, as announced in Budget 2023. Providing additional support to these businesses so they choose Canada for more than one stage in the manufacturing process would secure more jobs for Canadians and help cement Canada's position as a leader in this sector.

• electric vehicle assembly;
• electric vehicle battery production; and,
• cathode active material production.

For a taxpayer's building costs in any of the specified segments to qualify for the tax credit, the taxpayer (or a member of a group of related taxpayers) must claim the Clean Technology Manufacturing investment tax credit in all three of the specified segments, or two of the three specified segments and hold at least a qualifying minority interest in an unrelated corporation that claims the Clean Technology Manufacturing tax credit in the third segment. The building costs of the unrelated corporation would also qualify for the new investment tax credit.

The EV Supply Chain investment tax credit would apply to property that is acquired and becomes available for use on or after January 1, 2024. The credit would be reduced to 5 per cent for 2033 and 2034, and would no longer be in effect after 2034.

The EV Supply Chain investment tax credit is expected to cost $80 million over five years, starting in 2024-25, and an additional $1.02 billion from 2029-30 to 2034-35.

The design and implementation details of the EV Supply Chain investment tax credit will be provided in the 2024 Fall Economic Statement . Its design would incorporate elements of the Clean Technology Manufacturing investment tax credit, where applicable.

Delivering Major Economic Investment Tax Credits

To seize the investment opportunities of the global clean economy, we are delivering our six major economic investment tax credits. These will provide businesses and other investors with the certainty they need to invest and build in Canada. And they are already attracting major, job-creating projects, ensuring we remain globally competitive.

From new clean electricity projects that will provide clean and affordable energy to Canadian homes and businesses, to carbon capture projects that will decarbonize heavy industry, our major economic investment tax credits are moving Canada forward on its track to achieve a net-zero economy by 2050.

In November 2023, the government introduced Bill C-59 to deliver the first two investment tax credits and provide businesses with the certainty they need to make investment decisions in Canada today. Bill C-59 also includes labour requirements to ensure workers are paid prevailing union wages and apprentices have opportunities to gain experience and succeed in the workforce. With the support and collaboration of Parliamentarians, the government anticipates Bill C-59 receiving Royal Assent before June 1, 2024.

• Carbon Capture, Utilization, and Storage investment tax credit: would be available as of January 1, 2022;
• Clean Technology investment tax credit: would be available as of March 28, 2023; and,
• Clean Hydrogen investment tax credit; and,

The government will soon introduce legislation to deliver the next two investment tax credits:

• Clean Hydrogen investment tax credit: available as of March 28, 2023; and,
• Clean Technology Manufacturing investment tax credit: available as of January 1, 2024.

As a priority, the government will work on introducing legislation for the remaining investment tax credits, including the new EV Supply Chain investment tax credit, as well as proposed expansions and enhancements:

• Clean Electricity investment tax credit: would be available as of the day of Budget 2024, for projects that did not begin construction before March 28, 2023;
• The expansion of the Clean Technology investment tax credit would be available as of November 21, 2023; and,
• The expansion of the Clean Electricity investment tax credit would be available from the day of Budget 2024, for projects that did not begin construction before March 28, 2023.
• Clean Technology Manufacturing investment tax credit enhancements to provide new clarity and improve access for critical minerals projects. Draft legislation will be released for consultation in summer 2024 and the government targets introducing legislation in fall 2024.
• The EV Supply Chain investment tax credit : would be available as of January 1, 2024.

Given that the major economic investment tax credits will be available, including retroactively, from their respective coming into force dates, businesses are already taking action to break ground on projects that will reduce emissions, create jobs, and grow the economy. Passing the major economic investment tax credits into law will secure a cleaner, more prosperous future for Canadians today, and tomorrow.

Implementing the Clean Electricity Investment Tax Credit

As the economy grows, Canada's electricity demand is expected to double by 2050 (Chart 4.7). To meet this increased demand with a clean, reliable, and affordable grid, our electricity capacity must increase by 1.7 to 2.2 times compared to current levels (Chart 4.8). Investing in clean electricity today will reduce Canadians' monthly energy costs by 12 per cent (Chart 4.9) and create approximately 250,000 good jobs by 2050 (Chart 4.10).

Canada already has one of the cleanest electricity grids in the world, with 84 per cent of electricity produced by non-emitting sources of generation. Quebec, British Columbia, Manitoba, Newfoundland and Labrador, and Yukon are already clean electricity leaders and generate nearly all of their electricity from non-emitting hydropower—and have more untapped clean electricity potential. Other regions of Canada will require major investments to ensure clean, reliable electricity grids, and the federal government is stepping up to support provinces and territories with these investments.

In Budget 2023, the government announced the new Clean Electricity investment tax credit to deliver broad-based support to implement clean electricity technologies and accelerate progress towards a Canada-wide net-zero electricity grid.

• Low-emitting electricity generation systems using energy from wind, solar, water, geothermal, waste biomass, nuclear, or natural gas with carbon capture and storage.
• Stationary electricity storage systems that do not use fossil fuels in operation, such as batteries and pumped hydroelectric storage.
• Transmission of electricity between provinces and territories.
• The Clean Electricity investment tax credit would be available to certain taxable and non-taxable corporations, including corporations owned by municipalities or Indigenous communities, and pension investment corporations.
• Provided that a provincial and territorial government satisfies additional conditions, outlined below, the tax credit would also be available to provincial and territorial Crown corporations investing in that province or territory.
• Robust labour requirements to pay prevailing union wages and create apprenticeship opportunities will need to be met to receive the full 15 per cent tax credit.

The Clean Electricity investment tax credit is expected to cost $7.2 billion over five years starting in 2024-25, and an additional $25 billion from 2029-30 to 2034-35.

The Clean Electricity investment tax credit would apply to property that is acquired and becomes available for use on or after the day of Budget 2024 for projects that did not begin construction before March 28, 2023. The credit would no longer be in effect after 2034. Similar rules would apply for provincial and territorial Crown corporations, with modifications outlined below.

Provincial and Territorial Crown Corporations

The federal government is proposing that, for provincial and territorial Crown corporations to access to the Clean Electricity investment tax credit within a jurisdiction, the government of that province or territory would need to:

• Work towards a net-zero electricity grid by 2035; and,
• Provincial and territorial Crown corporations passing through the value of the Clean Electricity investment tax credit to electricity ratepayers in their province or territory to reduce ratepayers' bills.
• Direct provincial and territorial Crown corporations claiming the credit to publicly report, on an annual basis, on how the tax credit has improved ratepayers' bills.

If a provincial or territorial government satisfies all the conditions by March 31, 2025, then provincial or territorial Crown corporations investing in that jurisdiction would be able to access the Clean Electricity investment tax credit for property that is acquired and becomes available for use on or after the day of Budget 2024 for projects that did not begin construction before March 28, 2023.

If a provincial or territorial government does not satisfy all the conditions by March 31, 2025, then provincial or territorial Crown corporations investing in that jurisdiction would not be able to access the Clean Electricity investment tax credit until all the conditions have been satisfied. In this case, the Clean Electricity investment tax credit would apply to property that is acquired and becomes available for use from the date when the conditions are deemed to have been satisfied for projects that did not begin construction before March 28, 2023.

The Department of Finance Canada will consult with provinces and territories on the details of these conditions before legislation is introduced this fall.

Additional design and implementation details for the tax credit can be found in the Budget Tax Measures Supplementary Information, under "Clean Electricity investment tax credit."

Delivering Clean Electricity with Indigenous, Northern, and Remote Communities

The government has announced significant measures to advance clean electricity projects nationwide. These initiatives include the Clean Electricity investment tax credit, the Smart Renewables and Electrification Pathways Program, and strategic financing through the Canada Infrastructure Bank. Understanding the energy goals and challenges in Indigenous, Northern, and remote communities—such as moving away from diesel—the government has offered unique assistance for projects in these areas, including for planning and feasibility stages. Recent federal investments to support projects with these communities include:

• Up to $535 million in Canada Infrastructure Bank financing and $50 million in funding from the Smart Renewables and Electrification Pathways Program for the 250-MW Oneida Energy storage project in Ontario, which is the largest battery storage project in the country.
• $173 million in Canada Infrastructure Bank financing and $50 million in funding from the Smart Renewables and Electrification Pathways Program for the Bekevar Wind Power project, an Indigenous-led wind power project in Saskatchewan.
• $14.4 million in funding to explore the feasibility of the Kivalliq Hydro Fibre Link, an innovative project that would connect northern Manitoba to southeastern Nunavut to provide electricity and internet access to five communities and one existing mine, helping to transition Northern communities off of diesel and connect them to the rest of Canada.
• $9 million in funding from the Smart Renewables and Electrification Pathways Program for the Salay Prayzaan Solar project, which is 100 per cent owned by the Métis Nation of Alberta.

Implementing the Major Economic Investment Tax Credits

The government's suite of major economic investment incentives is unprecedented in Canadian history, and the government is delivering these supports on a priority basis to attract investment, create good-paying jobs, and grow the economy, while continuing to make progress in the fight against climate change.

To deliver the major economic investment tax credits, without delay, the government is boosting resources to the Canada Revenue Agency, Natural Resources Canada, and the Department of Finance Canada, which each have a role to play in delivering these support measures. To this end:

• Budget 2024 proposes to provide the Canada Revenue Agency up to $90.9 million over 11 years, starting in 2024-25, to administer the new major economic investment tax credits.
• Budget 2024 proposes to provide Natural Resources Canada $7.4 million over five years, starting in 2024-25, to provide expert technical advice on engineering and scientific matters related to the major economic investment tax credits and to support the administration of certain investment tax credits with the Canada Revenue Agency.
• Budget 2024 proposes to provide the Department of Finance Canada $21.4 million over 11 years, starting in 2024-25, to complete the implementation, including legislation, of the major economic investment tax credits, ensure ongoing evaluation and response to emerging issues, and propose appropriate legislative amendments to the Income Tax Act and Income Tax Regulations .

The Canada Growth Fund

The Canada Growth Fund is a $15 billion arm's length public investment vehicle launched by the federal government to attract private capital and invest in Canadian projects and businesses, which is led by Canada's world-leading public sector pension professionals. The Canada Growth Fund investments in clean energy and clean technology are already building Canada's strong, clean economy and creating good-paying jobs across the country:

• On October 25, 2023, the Canada Growth Fund made its first investment—a $90 million investment in a groundbreaking geothermal energy company, Calgary's Eavor Technologies Inc., that is creating meaningful employment opportunities for Albertans and securing the Canadian future of a company at the leading-edge of the global economy. 
• The Canada Growth Fund's second investment was announced on December 20, 2023—a $200 million direct investment, plus complementary carbon contract offtake agreement, in a world-leading carbon capture and sequestration company, Calgary's Entropy Inc. to support the reduction of up to one million tonnes of carbon per year. This major investment will support 1,200 good jobs for Albertans and grow the company's Canadian-based activities.
• The Canada Growth Fund's third investment was announced on March 25, 2024—a $50 million commitment into the Idealist Climate Impact Fund, a clean tech investment fund led by the Montréal-based Idealist Capital. The clean tech fund will manage equity investments into innovative entrepreneurs and businesses that are creating good-paying jobs and accelerating the energy transition.

Carbon Contracts for Difference

A price on pollution is the foundation of Canada's plan to build a prosperous net-zero economy. It is a system that is fair and that promotes market-driven solutions. The government recognizes the substantial demand from industry and other stakeholders for carbon contracts for difference (CCFDs) as a tool to accelerate investment in decarbonization and clean growth technologies by providing certainty around carbon pricing.

The 2023 Fall Economic Statement announced that the Canada Growth Fund will be the principal federal entity to issue CCFDs, including allocating, on a priority basis, up to $7 billion to issue all forms of contracts for difference and offtake agreements. The Canada Growth Fund is fulfilling this important role as a federal issuer of CCFDs. Building on its initial success, the Canada Growth Fund is assessing the opportunity to expand its carbon contract offerings and is developing approaches that can best serve the different carbon credit markets across Canada:

• Budget 2024 announces that the Canada Growth Fund is developing an expanded range of CCFD offerings tailored to different markets and their unique risks and opportunities. The Canada Growth Fund will continue offering bespoke CCFDs and carbon offtake agreements, with a focus on provinces contributing significantly to greenhouse gas emissions reductions.
• Building on the insights gained from these transactions, Budget 2024 announces the Canada Growth Fund will explore ways to broaden its approach, for example, by developing off-the-shelf contracts for certain jurisdictions and ways to offer these contracts on a competitive basis for a set amount of emissions reductions.
• The Canada Growth Fund has around $6 billion remaining to continue issuing, on a priority basis, all forms of CCFDs and carbon offtake agreements. Budget 2024 announces the government will ensure that the Canada Growth Fund continues to have the resources it needs to fulfill its role as federal issuer of CCFDs. The government is also evaluating options to enhance the Canada Growth Fund's capacity to offer CCFDs, including by exploring the possibility of a government backstop of certain CCFD liabilities of the Canada Growth Fund.

CCFDs can help develop robust carbon credit markets, and the federal government has taken action to ensure their success. For example, in 2022, Environment and Climate Change Canada worked with Alberta to ensure that their TIER market was sufficiently stringent so that the projected demand for carbon credits exceeded projected supply, ensuring robust credit demand even as more major decarbonization projects get built and more credits are generated.

Credit markets are largely the responsibility of provinces, and there are opportunities to improve how these markets function. For example, commitments to maintain their industrial carbon pricing systems over the long-term, tighten the stringency of systems as necessary to avoid an oversupply of credits, publishing the price of carbon credits, and recommitting to maintain a price signal of $170 per tonne by 2030 could help improve carbon price expectations for investors. Increased credit price transparency would greatly improve market functioning and provide greater investment certainty, unlocking more decarbonization projects. It would also facilitate the Canada Growth Fund's efforts to develop off-the-shelf CCFDs and deliver more deals, much quicker across provincial carbon markets.

• Budget 2024 announces that Environment and Climate Change Canada will work with provinces and territories to improve the functioning of carbon credit markets, in order to help unlock additional decarbonization projects throughout Canada.

Getting Major Projects Done

Putting Canada on a path to net-zero requires significant and sustained private sector investment in clean electricity, critical minerals, and other major projects. For these investments to be made, Canada's regulatory system must be efficient and quicker—it shouldn't take over a decade to open a new mine and secure our critical minerals supply chains.

To that end, Budget 2023 announced an intention to develop a plan to improve the efficiency of the impact assessment and permitting processes for major projects. The Ministerial Working Group on Regulatory Efficiency for Clean Growth Projects was launched to coordinate this work, and drive positive, pro-growth culture change throughout government, to ensure major project approvals come quicker. New major projects create thousands of new, good-paying jobs for Canadians, and the government is focused on getting more done.

• Provide $9 million over three years, starting in 2024-25, to the Privy Council Office's Clean Growth Office to implement the recommendations of the Ministerial Working Group and reduce interdepartmental inefficiencies, including preventing fixation on well-studied and low-risk impacts, ensuring new permitting timelines are upheld throughout departments, and improving data sharing between departments to reduce redundant studies.
• Launch work to establish a new Federal Permitting Coordinator within the Privy Council Office's Clean Growth Office.
• Set a target of five years or less to complete federal impact assessment and permitting processes for federally designated projects, and a target of two years or less for permitting of non-federally designated projects;
• Issue a Cabinet Directive to drive culture change , achieve new targets, and set out clear federal roles and responsibilities within and across departments with the objective of getting clean growth projects built in a timely and predictable manner;
• Build a Federal Permitting Dashboard that reports on the status of large projects which require permits, to improve predictability for project proponents, and increase the federal government's transparency and accountability to Canadians; and,
• Set a three-year target for nuclear project reviews , by working with the Canadian Nuclear Safety Commission and Impact Assessment Agency of Canada, and consider how the process can be better streamlined and duplications reduced between the two agencies.
• Amend the Impact Assessment Act to respond to the October 2023 Supreme Court of Canada decision that ruled that elements of the Act are unconstitutional. The proposed amendments will ensure the Act is constitutionally sound, facilitating efficient project reviews while advancing Canada's clean growth and protecting the environment. An amended Act will provide certainty for businesses and investors through measures that include increasing flexibility in substitution of assessments to allow for collaboration and avoid interjurisdictional duplication, clarifying when joint federal-provincial review panels are possible, and allowing for earlier Agency screening decisions as to whether a full impact assessment is required after the Planning phase. The amended Act will remain consistent with the United Nations Declaration on the Rights of Indigenous Peoples Act ;
• Enhance coordination across orders of government using the tools available under the Impact Assessment Act and permitting coordination mechanisms, to reduce duplication and minimize the burden of regulatory processes on project proponents and Indigenous groups; and,
• Engage Northern Premiers, Indigenous communities, industry, and other partners to discuss transformative changes to their unique project review frameworks, to ensure the North is also prepared to assess and build clean growth projects.
• Advance Indigenous participation in major projects, through the Indigenous Loan Guarantee Program detailed in Chapter 6, which will provide more opportunities for Indigenous communities to benefit from the significant number of natural resource and energy projects proposed to take place in their territories;
• Work to establish a Crown Consultation Coordinator to ensure efficient and meaningful Crown consultation with Indigenous peoples on the issuance of federal regulatory permits to projects that do not undergo federal impact assessments. The government will consult First Nations, Inuit, Métis, and Modern Treaty and Self-Governing Indigenous partners on the design of the Crown Consultation Coordinator. The Impact Assessment Agency of Canada will continue to be the Crown consultation body for all federal decisions related to projects that undergo federal impact assessments; and,
• Improve Indigenous capacity for consultation by advancing the co-development and implementation of consultation protocol agreements and resource centres, led by Crown-Indigenous Relations and Northern Affairs Canada.

More details on the Ministerial Working Group's recommendations will be published in an Action Plan in spring 2024. Additionally, further analysis of opportunities for improving the efficiency of the impact assessment process will be undertaken as part of the five-year review of the Impact Assessment Act's designated project list, which will occur later this year, following coming into force of the amended Act. This review will be undertaken in consultation with the public, including with Indigenous partners.

Getting major projects built means more jobs, in more regions across Canada, and more opportunities for the next generation of workers.

Securing the Canadian Biofuels Industry

Biofuels and biogas are renewable energy sources sustainably made from plants or biowaste, such as canola crops and landfill emissions. Not only do they generate fewer greenhouse gas emissions compared to fossil fuels, they also represent a unique opportunity for the Canadian economy. The industry supports agriculture and forestry jobs and can help decarbonize key sectors like marine, aviation, rail, and heavy industry. Canada's Clean Fuel Regulations , in place since 2022, are helping drive the production and adoption of specific biofuels in Canada.

The government is proposing new measures to support biofuels production in Canada, with a focus on renewable diesel, sustainable aviation fuel, and renewable natural gas, aiming to capitalize on the increasing demand for these fuels and strengthen Canada's position in the market. Budget 2024 announces:

• The government's intention to disburse up to $500 million per year from Clean Fuel Regulations compliance payment revenues to support biofuels production in Canada, subject to sufficient compliance payments being made to the federal government. More details will be announced in the 2024 Fall Economic Statement .
• The government will also retool the Clean Fuels Fund to deliver funding faster, and extend the Fund for an additional four years, until 2029-30. With reprofiled funding proposed through this extension, a total of $776.3 million will be available to be deployed from 2024-25 to 2029-30 to support clean fuel projects. The program will shift to a continuous intake process, and streamlined negotiations and decision-making processes will expedite delivery. By the end of this year, Natural Resources Canada will launch another call for proposals under the extended Clean Fuels Fund.
• The Canada Infrastructure Bank will invest at least $500 million in biofuels production under its green infrastructure investment stream.

Advancing Nuclear Energy, Nuclear Research, and Environmental Remediation

Non-emitting, nuclear energy is one of the key tools in helping the world reach net-zero emissions by 2050. Canada stands out as one of the few countries to have developed and deployed its own nuclear technology, the CANDU. And the robust Canadian supply chains built around CANDU not only generate high-skilled jobs and foster research and development but also play a role in creating affordable and clean electricity. Canada's nuclear sector also produces medical isotopes essential for radiation therapy and diagnosing heart disease.

Canada is a Global Nuclear Energy Leader

Over the last few years, the government has announced significant investments and action to advance nuclear energy:

Large Reactors:

• Canada has committed up to $3 billion in export financing to Romania to support the construction of two new CANDU reactors, reducing Romania's reliance on Russian energy while boosting their own energy security and their neighbours', all while supporting Canadian jobs. Canadian supply chains will participate in the construction and maintenance of these reactors over their multi-decade operating life.
• The government announced $50 million in funding to support Bruce Power's large nuclear expansion.

Small Modular Reactors (SMRs):

• The Canada Infrastructure Bank announced a $970 million investment to support Ontario Power Generation in building the first grid-scale SMR among G7 nations at Darlington.
• The Strategic Innovation Fund has committed $94.7 million to accelerate the development of three different next generation SMR designs.
• The government announced $74 million in funding to support SaskPower's SMR development.
• The government announced $120.6 million to enable the deployment of SMRs through various activities such as building regulatory capacity.

Major Economic Investment Tax Credits:

• The Clean Electricity and Clean Technology Manufacturing investment tax credits announced in Budget 2023 would support investments in nuclear electricity generation, nuclear power supply chains, and nuclear fuel production, which are part of the solution for a clean economy transition.

 Sustainable Finance:

• The government updated its Green Bond Framework to make certain nuclear energy expenditures eligible.

Budget 2024 is announcing new measures to help get nuclear projects built in a timely, predictable, and responsible fashion.

Canadian Nuclear Laboratories conducts nuclear science research that helps advance clean energy and medical technologies, as well as environmental remediation and waste management of historic nuclear sites. This work is overseen by Atomic Energy of Canada Limited, a Crown corporation responsible for enabling nuclear science and technology and ensuring environmental protection at nuclear sites.

• Budget 2024 proposes to provide $3.1 billion over 11 years, starting in 2025-26, with $1.5 billion in remaining amortization, to Atomic Energy of Canada Limited to support Canadian Nuclear Laboratories' ongoing nuclear science research, environmental protection, and site remediation work.

Canada-U.S. Energy Transformation Task Force

On March 24, 2023, the Canada-U.S. Energy Transformation Task Force was launched by Prime Minister Trudeau and President Biden, as a one-year joint initiative to support our collective energy security and economic growth as we transition to a clean energy future. Canada is pleased to announce the renewal of the Energy Transformation Task Force for an additional year.

Since its creation, the Energy Transformation Task Force has driven significant progress towards more secure and resilient Canada-U.S. supply chains for critical minerals, nuclear fuels, and green steel and aluminum.

Canada is a global leader in the supply of responsibly sourced critical minerals. The government is investing $3.8 billion through the Canadian Critical Minerals Strategy to further develop Canadian value chains for critical minerals needed for our green and digital economy, including the new Critical Mineral Exploration Tax Credit. The Strategy will be further enabled by enhancements to the Clean Technology Manufacturing investment tax credit, and Canada's new Electric Vehicle Supply Chain investment tax credit.

Canada is building on our strong partnership with the U.S. on critical minerals, underpinned by the Canada-U.S. Joint Action Plan on Critical Minerals Collaboration. Under the Energy Transformation Task Force, we have redoubled efforts to address issues of mutual concern such as bolstering supply security for critical minerals. Our government will continue to work in close collaboration with industry partners and our allies to support cross-border priority critical mineral projects that advance our shared interests.

Nuclear energy will play a key role in achieving net-zero greenhouse gas emissions. Canada is a Tier-1 nuclear nation with over 70 years of technological leadership, including our own national reactor technology, and a strong domestic supply chain that includes the world's largest deposit of high-grade natural uranium. Our government is taking action to support the growth of nuclear energy, including through the Clean Electricity investment tax credit, the Clean Technology Manufacturing investment tax credit, the Strategic Innovation Fund, the Canada Infrastructure Bank, and an updated Green Bond Framework that includes certain nuclear expenditures.

At COP28, the government and likeminded partners reaffirmed their commitment to triple nuclear energy capacity and promote public-private investment to strengthen supply chains and reduce reliance on non-allied countries for nuclear fuel needed for advanced and conventional nuclear energy. Through the Energy Transformation Task Force, Canada will continue to engage industry and international partners with a view to announcing concrete measures later this spring to bolster North American nuclear fuel supply chains.

Canadian steel and aluminum—among the greenest in the world—are important pillars of integrated North American manufacturing supply chains and key products to support the net-zero transition. We have invested significantly to further decarbonize our steel and aluminum sectors and to maintain their competitiveness in the green economy. As well, earlier this year, our government announced actions to increase the transparency of steel import data that will help provide more details on the origins of imported steel and align our practice with the U.S. We will continue to collaborate with the U.S. to promote common approaches for trade in low emissions green steel and aluminum goods.

Canada will continue to advance its work in partnership with the U.S., to reduce our shared exposure to production and supply chains controlled by non-likeminded countries, including by attracting investment in EV supply chains, solar, and more.

Clean Growth Hub

The Clean Growth Hub is the federal government's main source of information and advice on federal funding and other supports for clean technology projects in Canada. It directly supports up to 1,100 companies and organizations every year, ranging from emerging small businesses to Canada's world-leading clean tech companies.

Together, Innovation, Science and Economic Development Canada and Natural Resources Canada partner with 16 other departments and agencies to offer this one-stop shop to help businesses seeking to invest in Canada and create net-zero growth navigate the federal government's numerous clean economy programs and incentives—unlocking new investment and creating good jobs for Canadian workers.

• To continue supporting clean technology stakeholders to identify and access relevant support and advice, Budget 2024 proposes to provide $6.1 million over two years, starting in 2024-25, for the Clean Growth Hub.

Made-in-Canada Sustainable Investment Guidelines

The government recognizes the importance of promoting credible climate investment and combating greenwashing, to protect the integrity and fairness of the clean economy. This is critical for fostering investor confidence and mobilizing the private investment that Canada needs to help achieve a net-zero by 2050 economy.

As announced in the 2023 Fall Economic Statement, the Department of Finance Canada is working with Environment and Climate Change Canada and Natural Resources Canada to undertake next steps, in consultation with regulatory agencies, the financial sector, industry, and independent experts, to develop a taxonomy that is aligned with reaching net-zero by 2050. 

This work is being informed by the Sustainable Finance Action Council's Taxonomy Roadmap Report, which provided the government with recommendations on the design of a taxonomy to identify economic activities that the financial sector could label as "green" or "transition."  The government will provide an update on the development of a Canadian taxonomy later this year.

Find out more about the expected gender and diversity impacts for each measure in section 4.3 Growing Businesses to Create More Jobs

Small- and medium-sized businesses are an integral engine of Canada's economy, and they employ about 64 per cent of Canadian workers. Entrepreneurs, local small business, start-ups, growing medium-sized businesses—everywhere in Canada, there are people with good ideas, ready to grow their businesses and create good jobs. The government is ensuring Canada's investment climate sets businesses up for success.

For economic growth to reach the pace that is needed, existing businesses need support to stay competitive and scale-up. The government is taking action to help businesses scale-up their technological innovations, and implement productivity-raising technology across the economy. By cutting red tape, new and existing businesses can grow faster. Boosting access to financing from financial Crown corporations and encouraging Canada's large public pension funds to put their investments to work here at home will unlock new growth opportunities for Canadian businesses.

Through Budget 2024, the government is making it easier for new businesses to start-up and for existing businesses to grow by cutting red tape, and providing the tools businesses need to scale-up. The government is also taking steps to have Canadian public institutions and Crown corporations put their capital to work here at home and seize opportunities to increase Canada's growth and productivity.

The federal government has set up a range of programs and initiatives to help small and medium businesses thrive, and foster economic growth, including:

• Supporting small- and medium-sized businesses to hire 55,000 first year apprentices in construction and manufacturing Red Seal Trades through a grant of $5,000 towards upfront costs, such as salaries and training.
• Maintaining the lowest marginal effective tax rate (METR) in the G7, and a 5.2 percentage point competitive advantage over the average U.S. METR, to ensure Canada is a competitive place to do business.
• Secured commitments with Visa and Mastercard to lower credit card interchange fees for small businesses while protecting reward programs for consumers. These reductions are expected to save eligible Canadian small businesses approximately $1 billion over five years.
• Budget 2022 cut taxes for Canada's growing small businesses by more gradually phasing out their access to the small business tax rate.
• Ongoing support for small- and medium-sized businesses through Canada's seven Regional Development Agencies, including over $3.7 billion since 2018 to help businesses scale-up and innovate through the Regional Economic Growth through Innovation program.
• Almost $7 billion since 2018 for the Women Entrepreneurship Strategy to help women-owned businesses access the financing, networks, and expertise they need to start-up, scale-up, and access new markets.
• Enhancements to the Canada Small Business Financing Program, increasing annual financing to small businesses by an estimated $560 million.
• Up to $265 million for the Black Entrepreneurship Program to help Black business owners and entrepreneurs succeed and grow their businesses.
• $150 million investment in the Indigenous Growth Fund, to help recruit other investors, and in turn provide a long-term source of capital to support continued success for Indigenous businesses.
• $49 billion in interest-free, partially forgivable loans of up to $60,000 to nearly 900,000 small businesses and not-for-profit organizations through the Canada Emergency Business Account (CEBA).

National Regulatory Alignment

Barriers to internal trade are preventing Canada from reaching its economic potential. These barriers, most commonly the 13 different sets of regulations for each province and territory, hold back businesses from trading across provincial and territorial borders, restrict workers from moving between provinces and territories, and can increase costs for businesses as they work to overcome regulatory hurdles.

By addressing barriers to internal trade, including harmonizing regulations between provinces and territories, we can create more opportunities for Canadian businesses to grow and make life more affordable for all Canadians through greater competition and consumer choice. According to the International Monetary Fund, Canada could increase its gross domestic product (GDP) per capita by as much as 4 per cent—or $2,900 per capita estimated in 2023 dollars through the reduction of internal trade barriers for interprovincial trade of goods.

In 2022, the federal government launched the Federal Action Plan to Strengthen Internal Trade , which is guiding work with the provinces and territories to cut red tape. This includes a rigorous assessment of remaining federal exceptions in the Canadian Free Trade Agreement (CFTA) and important investments in trade data and research.

Two significant milestones have now been reached, with further actions upcoming in 2024:

• The removal and streamlining of one third of all federal exceptions in the CFTA. This means the removal of 14 exceptions related to procurement that will provide Canadian businesses more opportunities to compete to deliver government goods and services. By the end of 2024, the federal government will publicly release the rationale for all remaining exceptions, and encourages provinces and territories to do the same.
• The launch of the new Canadian Internal Trade Data and Information Hub on April 3, 2024. The Hub is an open and accessible data platform that will provide governments, businesses, and workers with timely, free information to help them make choices about where to invest and where to work. The Hub will help shine a light on where labour mobility barriers are highest and where unnecessary red tape costs businesses time and money.

The federal government is committed to working with provinces and territories to ensure goods, services, and workers move seamlessly across the country by advancing the mutual recognition of regulatory standards and eliminating unnecessary red tape for full labour mobility in the construction, health, and child care sectors.

• Budget 2024 announces that the government will launch the first-ever Canadian Survey on Interprovincial Trade in June 2024, to engage thousands of Canadian businesses on the challenges they face when buying, selling, and investing across provincial and territorial borders. The survey's insights will help identify top interprovincial barriers so that they can be eliminated.

As detailed in Chapter 1, the federal government is also leveraging federal housing financing to encourage provinces and territories to align their building codes, including to support modular housing construction, to make it easier to build more homes, faster.

The federal government will announce further progress to align the regulatory environment across the country in due course.

The New Canada Carbon Rebate for Small Businesses

Canada's small- and medium-sized businesses keep main streets flourishing across the country, create jobs, and deliver the dream of entrepreneurship. It is essential that these businesses thrive so they can continue being the bedrock of our communities and our economy.

Pollution has a cost, one which will only rise this century as climate change causes intensifying natural disasters and more severe health effects, as detailed in Chapter 5. Canada's carbon pricing system includes a federal backstop for provinces and territories that don't put their own system in place. It's a system designed to be fair and affordable—for households, Indigenous communities, farmers, and businesses—while reducing the pollution that is causing climate change.

The government is delivering on its commitment to return proceeds from the price on pollution to small- and medium-sized businesses, by announcing an accelerated and automated return process to provide direct refunds to small- and medium-sized businesses in the provinces where the federal fuel charge applies—the new Canada Carbon Rebate for Small Businesses.

• Proceeds would be returned directly to eligible corporations through direct payments from the Canada Revenue Agency (CRA), separately from CRA tax refunds.
• To receive their proceed return for each fuel charge year, corporations would be required to have filed their tax return for 2023 by July 15, 2024.
• The proposal would return proceeds for future fuel charge years, including 2024-25, in a similar manner each year.

Environment and Climate Change Canada continues to consult with Indigenous governments on how best to directly return fuel charge proceeds to their communities, and will announce next steps soon. The share of fuel charge proceeds allocated to Indigenous governments will double to 2 per cent of direct proceeds beginning this year.

Unlocking New Opportunity Through Financial Crown Corporations

Canada's financial Crown corporations support economic growth by helping businesses get the financing they need to grow; helping farmers and agri-businesses invest in new equipment and technology and support their operations; and helping companies sell their products around the world.

Canadians expect the government to make the most of their tax dollars. That is why in the 2023 Fall Economic Statement the government announced it would be reviewing the operations of the Business Development Bank of Canada, Export Development Canada, and Farm Credit Canada. Based on this review:

• The amended Framework has also introduced a target solvency rating for financial Crown corporations in cases where the Office of the Superintendent of Financial Institutions has no legislative supervisory role. The amended Framework can be found in the: Capital and Dividend Policy Framework for Financial Crown Corporations .
• The Business Development Bank of Canada should increase financing for promising new and high-growth businesses and accelerate reorientation of its venture capital investments toward emerging and higher-risk sectors to help attract more private capital.
• Export Development Canada should leverage its full toolkit and authorities, including by updating internal risk management guidance to facilitate greater risk taking across its portfolio. Recognizing that success for Canadian exporters in highly competitive markets and sectors at times requires additional targeted support, Export Development Canada should also create a new stretch capital envelope to maximize potential for exporters in areas of strategic importance for Canada by taking on greater risk in deploying its capital. Having Export Development Canada take on more higher-risk, higher-impact transactions itself will reduce the need for direct support through the Canada Account. Further implementation details, including the scale and scope of the envelope, will be identified over the coming months.
• Farm Credit Canada should continue to pursue opportunities to support agri-food and agribusiness, including through venture capital investment, and further deployment of technologies to mitigate climate change. The government intends to amend the Farm Credit Canada Act to require regular legislative reviews that ensure Farm Credit Canada's activities are aligned with the sector's needs.

In focusing their mandate on driving economic growth and productivity, these Crown corporations are also expected to prioritize new financing, insurance, and advisory support to under-financed business owners, as well as increase their public reporting and engagement with Canadians. The performance incentives of senior leaders are expected to align with their organizations taking on increased risk appetite in support of economic growth objectives. For Export Development Canada, performance incentives should also encourage alignment of business activities with countries that have free trade agreements with Canada.

Investing in Canadian Start-Ups

Venture capital financing gives Canadian entrepreneurs the resources they need to start-up, scale-up, and become the next generation of Canadian anchor companies. Financing can help take new ideas from lab to market, while creating high-quality, middle-class jobs.

The Venture Capital Catalyst Initiative (VCCI) strengthens Canada's venture capital ecosystem by co-investing with the private market, discovering and nurturing the next generation of globally recognized Canadian companies, and generating returns for private and public investors alike. Since 2016, the government has invested $821 million through VCCI, delivering support to over 300 companies across Canada.

• Building on this momentum, Budget 2024 proposes to provide $200 million over two years, starting 2026-27, on a cash basis, to increase access to venture capital for equity-deserving entrepreneurs, and to invest in underserved communities and outside key metropolitan hubs. 

Encouraging Pension Funds to Invest in Canada

Keeping Canada's vibrant economy strong for future generations of Canadians requires significant capital investments in our businesses, industries, and communities. Attracting higher levels of investment into Canada from all sources, including foreign and domestic private and institutional investors will raise Canada's productivity and increase living standards for all Canadians.

Pension plans are a critical pillar in Canada's retirement income system that ensures Canadians can enjoy a secure and dignified retirement. Canadian pension funds hold over $3 trillion in assets, which are invested both at home and abroad to provide secure retirement income for plan members and retirees.

The government believes that encouraging pension funds to invest in Canada more would help grow the Canadian economy and provide the stable long-term returns needed to deliver strong pensions for Canadians. In the 2023 Fall Economic Statement , the government committed to improving transparency around pension funds' investments and to working collaboratively with Canadian pension funds to create an environment that encourages and identifies more domestic investment opportunities for pension funds and other responsible institutional investors.

Canadian pension funds rely on their strong governance practices and diversified portfolios to deliver Canadians' pensions, with assets including public and private equity, infrastructure, real estate, and bonds. Canada's own economy is full of investment opportunities in these asset classes that could provide valuable contributions to pension fund portfolios. Opening up more opportunities for investment by pension funds in these domestic assets would help one of Canada's largest pools of savings contribute to the growth of the Canadian economy.

Further engagement with industry experts and pension funds will guide the government's way forward on ways to make more domestic investments available that meet the needs of pension funds.

• digital infrastructure and AI investment;
• physical infrastructure;
• airport facilities;
• venture capital investments;
• building more homes, including on public lands; and,
• the removal of the 30 per cent rule for domestic investments.

To support investments in airport facilities, the Minister of Transport will release a policy statement this summer that highlights existing flexibilities under the governance model for Canada's National Airport System airports to attract capital, including from pension funds.

• Following up on the 2023 Fall Economic Statement , Budget 2024 also proposes to amend the Pension Benefits Standards Act, 1985 to enable and require the Office of the Superintendent of Financial Institutions to publicly release information related to the plan investments of large federally regulated pension plans.

The information to be disclosed would be set out in regulations and would include the distribution of plan investments by jurisdiction and, within each jurisdiction, by asset class.

The government will continue to engage with provinces and territories to discuss similar disclosures by Canada's largest pension plans in a simple and uniform format.

Boosting Regional Economic Growth

To build a brighter future for communities across the country, Canada's Regional Development Agencies help businesses and innovators grow to fuel economic growth and create good middle class jobs. Through the Regional Economic Growth through Innovation program, businesses can access funding to scale-up, implement new technologies, improve productivity, and find new markets, helping to develop prosperous and inclusive communities across the country.

• To create jobs and boost regional economic growth, Budget 2024 proposes to provide an additional $158.5 million over two years, starting in 2024-25, on a cash basis, to Canada's Regional Development Agencies for the Regional Economic Growth through Innovation program. A portion of this funding will be dedicated to housing innovation.

This support builds on the $200 million that Regional Development Agencies will deliver to businesses for AI adoption.

Cutting Red Tape to Boost Innovation

For innovative businesses to scale-up new ideas, they need certainty that they will be able to bring their product to market. But existing regulation can often be too outdated to fit the needs of new technologies.

To ensure regulation keeps pace with the speed of new innovations, rather than hold innovation back, the government is advancing work on regulatory "sandboxes" to create temporary rules to enable testing of products, services, or new regulatory approaches.

• Budget 2024 announces the government's intent to introduce amendments to the Red Tape Reduction Act to broaden the use of regulatory sandboxes across government. The changes will enable innovation by offering limited exemptions to existing legislation and regulations, streamlining the regulatory system, and reforming regulations to modern business realities.

Supporting the Canadian Chamber of Commerce's Business Data Lab

Since 2022, the Canadian Chamber of Commerce has collaborated with Statistics Canada to provide Canadian businesses with insights and information through the Business Data Lab. This initiative provides access to real-time information and analysis, that helps Canadian businesses stay informed, and make decisions that help them stay strong and support workers.

• To advance this work, Budget 2024 proposes to provide $7.2 million over three years, starting in 2024-25, to support the Canadian Chamber of Commerce's Business Data Lab.  

4.4 A Strong Workforce for a Strong Economy 

Find out more about the expected gender and diversity impacts for each measure in section 4.4 A Strong Workforce for a Strong Economy

Building an economy that is fair for everyone means making sure that every generation can seize the opportunities of the government's investments to grow the economy and create jobs.

Investing in new jobs and skills support for younger Canadians will help them get that first good job or start their first business. Strengthening labour laws and safeguarding the rights of workers will help ensure more jobs are good jobs. Skills and education investments for the next generation of workers will lead to higher productivity and benefit businesses in Canada and looking to invest in Canada who can tap into a robust, highly skilled workforce.

The federal government's generational job-creating investments today lay the groundwork for a brighter tomorrow, where good job opportunities are available to everyone.

• Helping over one million Canadians each year upgrade their skills or find new jobs by investing nearly $3 billion annually in Canada's Labour Market Development Agreements and Workforce Development Agreements with provinces and territories.
• Supporting a trades workforce that is skilled, inclusive, certified, and productive through the Canadian Apprenticeship Strategy.
• Equipping close to 105,000 Canadian workers with the skills they need by increasing access to union-led training through the Union Training and Innovation Program since 2019-20, and supporting over 45,000 apprentices through interest-free Canada Apprentice Loans since 2018-19.
• Introducing labour requirements for prevailing union wages and apprenticeship opportunities in most major economic investment tax credits to ensure Canadian workers thrive in the growing clean economy.
• Ensuring workers have time to recover when they get sick, by providing ten days of paid sick leave for all federally regulated workers.
• Banning the use of replacement workers during a strike or lockout in federally regulated workplaces to protect workers' right to strike and support a fairer collective bargaining process during labour disputes.

Empowering Young Entrepreneurs 

Futurpreneur Canada is a national not-for-profit organization that provides young entrepreneurs with access to financing, mentorship, and other business supports to help them launch and grow their business. For over two decades, Futurpreneur Canada's programs and offerings, supported by $161.5 million in federal funding, have helped over 17,700 young entrepreneurs to launch more than 13,900 businesses across the country, supporting thousands of jobs since its inception.

• To empower young entrepreneurs, Budget 2024 proposes to provide $60 million over five years, starting in 2024-25, for Futurpreneur Canada. Futurpreneur Canada will match this federal investment with funding received from other orders of government and private sector partners.

By 2029, Futurpreneur Canada estimates this investment will enable an estimated 6,250 additional youth-owned businesses to launch and scale-up their businesses.

Futurpreneur Helps Young Entrepreneurs Scale-up Their Businesses

Sarah is a recent university graduate who wants to launch a sustainable clothing manufacturing company, but is unsure where to begin. She learns about Futurpreneur Canada. After visiting their website, she finds resources to help develop and test her business model, write a business plan and even attends a webinar to answer her questions. Now, Sarah feels confident and prepared to launch her business, but is having difficulty securing financing.

She decides to apply to Futurpreneur's Startup Program to take advantage of their financing and mentorship offering. Futurpreneur helps her finalize her business plan and cash flow, collects the necessary documentation, reviews her application and determines her business is a good fit, and provides her with financing and mentoring to help launch her business and start making sales.

Sarah is matched with an experienced business mentor who will provide her with guidance and reassurance over the next two years and receives financing of up to $20,000 from Futurpreneur and up to $40,000 from BDC to help start her business. She is also connected to various networking events with experts and other young entrepreneurs to build her business network and gain peer advice.

Investing in a Strong Workforce for a Strong Economy

Investments since Budget 2017 in skills training measures include:

Labour Market Transfer Agreements: Annual investment of nearly $3 billion enabling provinces and territories to deliver training and employment supports tailored to their unique labour market needs.

Union-based training: Over $200 million through Budget 2022 and Fall Economic Statement 2022 to expand the Union Training and Innovation Program to train more than 30,000 additional apprentices and journeypersons.

Employer-led training: Budget 2021 announced the Sectoral Workforce Solutions Program to help key sectors of the economy, including the construction sector, implement solutions to address their current and emerging workforce needs. Budget 2021 also announced $250 million for the Upskilling for Industry Initiative to support more than 15,000 workers. Budget 2024 proposes $50 million over four years to provide skills training for workers in sectors disrupted by AI, and $10 million over two years to train more early childhood educators, building up the talent needed for the expansion of affordable, high-quality child care.

Apprenticeship Service: Launched the Apprenticeship Service to help first year apprentices in construction and manufacturing Red Seal trades connect with opportunities at small and medium-sized employers. Budget 2024 proposes to provide $90 million over two years for the Apprenticeship Service to help create placements in the residential construction sector.

Skilled Trades Awareness and Readiness Program:   Budget 2018 announced the Skilled Trades Awareness and Readiness Program to help Canadians explore the trades and make informed career choices. Budget 2024 proposes $10 million over two years to continue to encourage Canadians to explore and prepare for careers in the skilled trades.

Sustainable Jobs Training Fund:   Recently launched the Sustainable Jobs Training Fund to help workers upgrade or gain new skills for jobs in the low-carbon economy.

Indigenous-led training: $99.4 million per year through the co-developed Indigenous Skills and Employment Training (ISET) Program to help Indigenous people improve their skills and find employment.

Financial support for adult learners: About $250 million per year for the Canada Training Credit, which covers up to 50 per cent of eligible training fees.

Affordability for Apprentices: Eliminated Elimination of interest on Canada Apprentice Loans, which provides up to $4,000 per period of technical training for tuition, tools, equipment, living expenses and forgone wages.

Apprenticeship Requirements for Clean Economy Investment Tax Credits: to access the highest tax credit rates, projects must dedicate at least 10 per cent of labour hours performed by covered workers to apprentices. This provides apprentices with the crucial hours they need to complete their training.

Establishing a Right to Disconnect

Everyone needs some downtime; it is essential for well-being and mental health. As the nature of work in many industries has become increasingly digital, workers are finding it increasingly difficult to disconnect from their devices and inboxes after hours and on weekends. This has particularly impacted Millennial and Gen Z workers, many of whom have worked their whole careers without firm separation between work and personal time. 

The government is taking action to restore work-life balance for the many workers in federally regulated industries, including but not limited to financial services, telecommunications, and transportation, by moving forward with a right disconnect from work, outside of their working hours.

• This is expected to benefit up to 500,000 employees in federally regulated sectors.

Further, on the topic of worker misclassification, Employment and Social Development Canada and the Canada Revenue Agency will enter into necessary data-sharing agreements to facilitate inspections and enforcement.

Modernizing the Employment Equity Act

Through the Employment Equity Act , the government promotes and improves equality and diversity in federally regulated workplaces. Since the introduction of the Employment Equity Act , continued progress has been made to address inequalities, but some workers are still facing barriers to employment and many federal workplaces fail to reflect the full diversity of Canada's population. That is why, in 2021, the government launched an arm's length Task Force to review the Act and advise on how to modernize the federal employment equity framework.

• Following the recommendations of the Task Force, Budget 2024 announces the government's intention to propose legislative amendments to modernize the Employment Equity Act , including by expanding designated equity groups.

Examining Critical Port Operations

Labour disputes and work stoppages at Canadian ports can lead to serious economic impacts by disrupting supply chains. To protect port workers and resolve the structural issues underlying port labour disputes, in 2023, the government launched the first phase of a formal review in collaboration with industrial relations experts.

• Budget 2024 proposes to provide $3.1 million over two years, starting in 2024-25, to enable the Labour Program at Employment and Social Development Canada to complete the second phase of its review, which will explore long-term solutions to minimize labour disputes, respect the collective bargaining process, and secure the stability of Canada's supply chains. This funding would be sourced from existing departmental resources.

Extending Temporary Support for Seasonal Workers

Many seasonal workers—including in fishing and tourism sectors in Atlantic Canada and Quebec—rely on Employment Insurance for the support they need between work seasons. To address gaps in Employment Insurance support between seasons, the government introduced temporary rules in 2018 to provide up to five additional weeks—for a maximum of 45 weeks—to eligible seasonal workers in 13 economic regions. This support is set to expire in October 2024.

• Budget 2024 proposes to extend this support for seasonal workers in targeted regions until October 2026. The cost of this measure is estimated at $263.5 million over four years, starting in 2024-25. 

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