2SLGBTQIA+ Consumer Health Books

Happy Pride, all! Did you know that members of the 2SLGBTQIA+ community are more likely to experience health disparities than their heterosexual and cisgender counterparts?1 Many members of this community say they feel uncomfortable accessing healthcare and report facing discrimination because of who they are.2 Not on our watch!

The Wendy Patrick Consumer Health Collection has books to help members of the 2SLGBTQIA+ community take their health into their own hands. Here is a sample of some the books in our collection:

Naked Nutrition: An LGBTQ+ Guide to Diet and Lifestyle by Daniel O’Shaughnessy

“As a gay man living in London and working as a nutritionist, Daniel O’Shaughnessy knows that the LGBTQ+ community has specific dietary and health needs. Yet while there is huge demand for this kind of information in his private practice, there is very little reliable public information out there for the community to access. Naked Nutrition seeks to change that: it is the first LGBTQ+ focused guide to diet and lifestyle, taking an honest, inclusive and non-judgemental approach to the questions Daniel is asked most frequently. It covers a wide range of subjects, giving detailed, practical advice on matters including: weight loss and muscle gain, digestive health issues, addiction, sex, fertility, nutrition for balancing hormones while transitioning, how to eat if you have a chronic condition, and how to mitigate against the party lifestyle.”

Like a boy but not a boy : navigating life, mental health, and parenthood outside the gender binary by Andrea Bennett

“Like a Boy but Not a Boy explores author andrea bennett’s experiences with gender expectations, being a non-binary parent, and the sometimes funny and sometimes difficult task of living in a body. The book’s fourteen essays also delve incisively into the interconnected themes of mental illness, mortality, creative work, class, and bike mechanics (apparently you can learn a lot about yourself through truing a wheel).”

How to understand your gender : a practical guide for exploring who you are by Alex Iantaffi & Meg-John Barker

“Have you ever questioned your gender identity? Do you know somebody who is transgender or who identifies as non-binary? Do you ever feel confused when people talk about gender diversity? This down-to-earth guide is for anybody who wants to know more about gender, from its biology, history, and sociology to the role it plays in our relationships and interactions with family, friends, partners, and strangers. Activities throughout the book will engage people of all genders in a thoughtful, practical way, and help you understand people whose gender might be different from your own.”

I am ace: advice on living your best asexual life by Cody Daigle-Orians

“How do I know if I’m actually asexual? How do i come out as asexual? What kinds of relationships can I have as an ace person? If you are looking for answers to these questions, Cody is here to help. Within these pages lie all the advice you need as a questioning ace teen. Tackling everything from what asexuality is, the asexual spectrum, and tips on coming out, to intimacy, relationships, aphobia, and finding joy, this guide will help you better understand your asexual identity alongside deeply relatable anecdotes drawn from Cody’s personal experience. Whether you are ace, demi, gray-ace, or not sure yet, this book will give you the courage and confidence to embrace your unique self.”

Gender confirmation surgery : a guide for trans and non-binary people by Edward Whelan & Nicholas Avigdor Melamed

“With personal stories and illustrations throughout, this comprehensive resource will help you understand the full range of surgical options available. Information and advice about each procedure is offered, including planning and recovery, sexual health and fertility, and insight into what to expect in the years following an operation. This is essential reading for any trans or non-binary person considering gender confirmation surgery and will help you make the decision that’s right for you”

Am I trans enough? : how to overcome your doubts and find your authentic self by Alo Johnston

“Alo Johnston has been where you are. From watching every transition story on YouTube and navigating online message boards for answers to finally starting testosterone and transitioning himself, he now walks alongside you every step of the way to guide you towards acceptance of who you truly are. Born out of thousands of hours of research and conversations with hundreds of trans people, Am I Trans Enough? digs deep into internalized transphobia and the historical narratives that fuel it. It unveils what happens after you come out, or begin questioning living as a trans person, in a world that works against you. Use this book as a space to engage with your fears and explore your doubts without the pressure of needing to be a perfect trans representative. If you are just beginning your trans journey, are twenty years into transition or have no idea if you are even trans at all, this book will help you to become your most authentic self”

You can check out these books and many others on the main floor of the library. You can also visit our catalog to browse more titles in the Wendy Patrick Consumer Health Collection.

Have a safe and happy Pride! From the staff at the Schulich Library of Physical Sciences, Life Sciences, and Engineering

  1. Alliance for Healthier Communities. 2SLGBTQ+. (n.d.). Retrieved June 7, 2024, from https://www.allianceon.org/2SLGBTQ#:~:text=Two%20Spirit%2C%20Lesbian%2C%20Gay
    %2C,to%20stigma%2C%20discrimination%20and%20social ↩︎
  2. Mills, S., Dion, M., Thompson-Blum, D., Borst, C., & Diemert, J. (2019). Mapping the Void: Two-Spirit and LGBTIQ+ Experiences in Hamilton. ↩︎

Health Sciences Librarian-Approved Tools: Yale MeSH Analyzer

Sometimes, the search process happens backwards. What I mean by that is that you may find yourself with a stack of articles that you know you want to include in your review, but are then tasked with coming up with the search that will generate these articles and articles like them. The Yale MeSH Analyzer is here to facilitate the task and help you come up with a great list of search terms.

Developed by the team at the Yale University Cushing/Whitney Medical Library, this tool allows you to analyze those perfect articles and extract the Medical Subject Headings (MeSH) and keywords. This is definitely quicker than scanning each article manually.

Here’s how it works! You’ll be asked to enter the PubMed Identification Numbers (PMIDs) for your articles. You can find these on the individual article pages on PubMed:

You can enter up to 20 PMIDs at once for the tool to analyze. Once you’ve entered all your articles, the Yale MeSH analyzer will spit out a handy table, either online or in an downloadable Excel sheet, that allows you to see what MeSH terms the articles have been tagged with. And, despite what the name suggests, it isn’t limited to MeSH terms. The tool will also give you a list of keywords that the authors have used to describe their own articles.

Let’s take a look at an example: A student is interested in looking at experiences of young menstruating individuals in low-income countries and rural areas. She has managed to find four articles through her limited Google search, but would like to build a more comprehensive search in a few medical databases.

Want to follow along? Access the Yale MeSH Analyzer here. The PMIDs of the articles are as follows:

  • 30611223
  • 24244435
  • 29485336
  • 26436841

This is what the MeSH Analyzer produces for her (click on the image to enlarge it):

All four of the articles are tagged with the MeSH term Menstruation, so that’s a pretty good indication that our student should include it in her search! But one of the articles is also tagged with Menarche and that’s something that our student hadn’t thought to include. Other MeSH terms to think about are Rural Populations, Sanitation and Health Knowledge, Attitudes, Practice.

The author keywords are also telling:

One article mentions Menstrual hygiene management and the other Menstrual hygiene products. This gives you insight into the various ways different researchers are referring to the same concept. In addition, it allows you to parse your search. Instead of writing every iteration, you can choose to just add menstrual hygiene as a term to ensure that you’re picking up all the varieties.

Congratulations! You now have a great foundation on which to build your search!

Try the Yale MeSH Analyzer for yourself!

More AI Tools: Using Gemini for PubMed Searches

In a previous blog post, we discussed the use of ChatGPT for PubMed searches. Now that Gemini, previously Bard from Google, has become available in Canada, it’s worth taking a look at how the technology interprets requests for PubMed searches and how it differs from ChatGPT.

We’ll be using the exact same example that we used previously:

In former smokers with chronic obstructive pulmonary diseases, is pulmonary rehabilitation an effective treatment method?

When asked to generate a MeSH only search string, this is what the program comes up with:

("Chronic Obstructive Pulmonary Disease"[Mesh] AND "Pulmonary Rehabilitation"[Mesh]) AND ("Exp-Smokers"[Mesh] AND "Smoking Cessation"[Mesh]) [Optional: AND "Treatment Outcome"[Mesh]]

It seems like all AI programs share a brain cell when it comes to MeSH. Like ChatGPT, Gemini is making up its own MeSH terms – Pulmonary Rehabilitation and Chronic Obstructive Pulmonary Disease are not MeSH terms! It has also included Exp-Smokers, when the correct MeSH term is Ex-Smokers. Yikes.

Another obvious issue is the use of AND to combine “Exp-Smokers”[Mesh] and “Smoking Cessation”[Mesh]. In the context of this question, these are synonyms, part of the same concept, and would therefore be combined using OR.

Let’s take a look at Gemini’s response as a whole:

While ChatGPT just spits out MeSH terms, Gemini seems to take it one step further and offer advice. The use of Required and Optional is presumptuous, but I can appreciate that the program tries to explain what kind of articles your query will return.

The biggest problem, and the one I take the most issue with, is the program’s declaration that adding the MeSH term Treatment Outcome will broaden your search. While Treatment Outcome might be a broad term, including this MeSH term will severely narrow your search and potentially eliminate relevant articles. The more concepts you combine using the Boolean operator AND, the less results you will end up with.

When asked to generate a list of keywords, Gemini provided one or two synonyms for each concept, even though we know from the complete search we generated in the last post that there are way more out there:

If you’re going to be using any AI program to generate synonyms, it’s best to enter your terms one at a time, and only after you’ve combed through other sources, like relevant journal articles or a thesaurus.

Gemini is also struggling with the concept of Booleans. The search string it generates is riddled with Boolean errors:

(COPD OR ex-smoker) AND pulmonary rehabilitation AND (exercise training OR dyspnea OR quality of life)

My head hurts just looking at this… I’ve used different colours to group together keywords of the same concept. COPD and ex-smoker are two entirely different concepts and should not be grouped together. If exercise training is often used in conjunction with pulmonary rehabilitation like Gemini says, then why is it not being combined using OR? Presumably, Gemini thinks that dyspnea and quality of life are important outcomes, so the use of OR to combine them is not false, but it shouldn’t have grouped exercise training with them. Overall, it’s a mess.

One thing that I think Gemini does better than ChatGPT is the explanation for searching both MeSH terms and keywords, and I trust it more as a learning tool than as a search string generator:

If you’re still not sure why you should complement your MeSH terms with appropriate keywords, take a look at our Health Sciences FAQ.

By now you’re probably thinking, “Gee, I don’t think Gemini could possibly get any more wrong.” Well, BUCKLE UP! I asked Gemini how to exclude keywords, wanting to know how it would explain the Boolean operator NOT:

Here, Gemini is telling you that instead of using NOT, you can simply use AND – to exclude a term. That’s not how that works! Booleans are sacred! They’re as old as time itself! You can’t just go around changing them! PubMed’s own User Guide warns that the minus sign (-) is converted to a space. Consequently, if you wanted to retrieve articles that mention COPD but not bronchitis, Gemini would have you type this: (“COPD” AND – “Bronchitis”). But PubMed isn’t interpreting this search the way Gemini thinks it is:

PubMed is ignoring your minus, turning it into a space, and searching instead for articles that mention both COPD and bronchitis. The correct way to exclude bronchitis is by entering: “COPD” NOT “bronchitis”.

I’m not ready to give you my blessing to generate searches using ChatGPT or Gemini. Maybe one day we’ll get there, but for now, I think I’ve demonstrated why you shouldn’t trust either of these tools with the job. The good news is that if you’re struggling with searching in PubMed or other medical databases, your librarians are here to help you! Contact us with any questions relating to searches and databases.

Using ChatGPT for PubMed Searches: Be Smart!

With the advent of new AI technologies geared at making academic life simpler, it can be tempting to try to use these tools to help when in unchartered territory. And while some of these tools like ChatGPT can be helpful starting points, they should not be relied on entirely for your medical research. 

PubMed is a freely available biomedical database that has over 36 million records from leading journals in the medical field. It is usually the starting point for most medical research. However, it is not always easy to navigate, and some researchers struggle with building a comprehensive search. Although PubMed is taught to medical students at the undergraduate level and to other students in the health sciences, it can take a while before you feel comfortable searching on your own. 

Searching in PubMed requires using a balance of the controlled vocabulary terms, MeSH terms, and keywords. These are meant to complement each other and allow you to find the most complete set of results.

ChatGPT can definitely help you get started, and even make helpful suggestions, but we want to make sure you’re using it properly! 

When doing any kind of searching, it is important to break down your research question to its basic searchable components. In the health sciences, we point people to the PICO model, which allows you to identify the patient population (P), the intervention (I), the comparator (C) and the patient outcomes (O). We would then combine these components to come up with a searchable question. 

For the purposes of this blog post, we will be using the following research question as an example: In former smokers with chronic obstructive pulmonary diseases, is pulmonary rehabilitation an effective treatment method?

We can start by asking ChatGPT to tell us the MeSH terms that are appropriate for use in this question:

Looks good, right? Wrong! 

Let’s talk about what works, first. ChatGPT does a great job of breaking down the question and telling you what concepts should be searched. The way the search is entered into the database is also correct. In PubMed, search terms should always be entered between quotation marks and the search fields should be entered in square brackets.

The most glaring problem is that ChatGPT has made up MeSH terms. Pulmonary Rehabilitation is not a MeSH term! Neither is Former Smokers!  If you enter “Pulmonary Rehabilitation”[mesh] or “Former Smokers”[mesh] into PubMed as ChatGPT suggests, you would get zero results. The closest MeSH term for Former Smokers is Ex-Smokers, but there is no close MeSH term for Pulmonary Rehabilitation.

Although ChatGPT’s suggestions are valuable, you always need to check the MeSH database for the accuracy of the terms provided. 

Next, let’s take a look at what ChatGPT will generate when asked to build a keyword search: 

(“Former Smokers” OR “Smoking Cessation” OR “Tobacco Use Cessation”) AND (“Chronic Obstructive Pulmonary Disease” OR COPD) AND (“Pulmonary Rehabilitation” OR “Respiratory Therapy” OR “Exercise Therapy for Lungs”) AND (“Treatment Effectiveness” OR “Therapeutic Efficacy” OR “Outcome Assessment”)

One of the first things that jumps out at me, and what I’ve written in red, is the acronym COPD. While it is not incorrect to enter the acronym as a keyword, the lack of quotation marks is what worries me. In failing to add quotation marks, PubMed triggers Automatic Term Mapping, a problematic feature that adds unnecessary search terms and results based on what the database thinks you are searching for. 

The image below will show you the difference between searching with and without quotation marks:

PubMed has translated the search to include the correct MeSH term, but also include the individual words (disease, pulmonary, obstructive, chronic) as their own, stand-alone keywords. Are you still going to find articles related to chronic obstructive pulmonary disease? Sure. But there are certainly going to be more irrelevant articles for you to sift through. Just look at the difference in the numbers – 109 thousand as compared to 62 thousand! 

Another problem with ChatGPT is its inclusion of outcome search terms. We don’t normally build a search with outcomes, especially such generic ones. Instead, outcomes are screened for once we have our set of results. You will certainly find articles about pulmonary rehabilitation in former smokers with COPD that don’t use terms like “treatment effectiveness” and “outcome assessment” in the title and abstract. By putting these terms in the search, you are forcing the database to look for them and consequently eliminating relevant results. 

When asked to generate a search with MeSH terms and keywords for our initial question, ChatGPT combines what we’ve seen above and gives you one big search string to enter into the database: 

(“Former Smokers”[MeSH] OR “Smoking Cessation”[MeSH] OR “Tobacco Use Cessation”[MeSH] OR “Former Smokers” OR “Smoking Cessation” OR “Tobacco Use Cessation”) AND (“Chronic Obstructive Pulmonary Disease”[MeSH] OR COPD OR “Chronic Obstructive Pulmonary Disease”) AND (“Pulmonary Rehabilitation”[MeSH] OR “Respiratory Therapy”[MeSH] OR “Exercise Therapy for Lungs”[MeSH] OR “Pulmonary Rehabilitation” OR “Respiratory Therapy” OR “Exercise Therapy for Lungs”) AND (“Treatment Effectiveness”[MeSH] OR “Therapeutic Efficacy”[MeSH] OR “Outcome Assessment”[MeSH] OR “Treatment Effectiveness” OR “Therapeutic Efficacy” OR “Outcome Assessment”) 

At the time of this writing, this search yields two results. TWO! In fact, PubMed isn’t too happy with our search either, and issues the following warning: 

Not only has it kept the two made-up MeSH terms that we already saw, but it’s created a few new ones, too, including, Exercise Therapy for Lungs, Treatment EffectivenessTherapeutic Efficacy and Outcome Assessment. And Chronic Obstructive Pulmonary Disease is not technically a MeSH term. The correct MeSH term is Pulmonary Diseases, Chronic Obstructive. MeSH terms need to be exact.

Do better, ChatGPT…

Now, let’s take a look at a search that I, a health sciences human librarian made for the same research question:

(“Ex-Smokers”[MeSH] or “Tobacco Use Cessation”[mesh] or “ex smoker*” or “exsmoker*” or “former smoker*” OR ((“history” AND (“smoking” or “cigarette*” or “tobacco”))) AND (“Breathing Exercises”[Mesh] or “breathing exercise*” or “pulmonary rehab*” or “respirat* rehab*” or “respiratory muscle training” or “breath* control*” or “lung rehab*” or “lung exercise*” or “respiratory exercise*”)) AND (“Pulmonary Disease, Chronic Obstructive”[mesh] or “Chronic Obstructive Lung Disease*” or “Chronic Obstructive Pulmonary Disease*” or “COPD” or “COAD” or “Chronic Obstructive Airway Disease*” or “Chronic Airflow Obstruction*” or “Chronic Bronchitis” or “Pulmonary Emphysema*” or “Centrilobular Emphysema*” or “Panlobular Emphysema*” or “Focal Emphysema*”)

This search combines the correct MeSH terms with keywords and synonyms. I brainstormed different terms for all of the key concepts, and included conditions that fall under the umbrella term of chronic obstructive pulmonary diseases. All search terms are nicely encased in quotation marks to avoid automatic term mapping and I used truncation to account for different spellings. This search yielded 157 results.

Eat your heart out, ChatGPT!

While ChatGPT may not be great at making your search strategy, it can be useful. It can help you break down your question into concepts and offer suggestions during the brainstorming process. Try asking the program to generate synonyms for words – it might bring up things that you never thought of before. 

For example, I asked ChatGPT to generate a list of synonyms for cancer: 

They’re not all winners, and I wouldn’t enter them all into a search engine, but maybe I didn’t think to include malignancy and this was a great reminder. Or maybe I didn’t think to truncate the word cancer as cancer* to include terms such as cancers or cancerous, or to truncate malignan* to account for malignant, malignancy or malignancies. Thanks, ChatGPT! While I don’t recommend using ChatGPT for everything, using it as a thesaurus can be quite fruitful.

Try these tips out the next time you use ChatGPT, or any other AI program, and see the difference in your searches. Don’t forget to contact your librarians for specific questions related to PubMed. You can find a list of librarians by subject matter here.

Introducing the Health Sciences FAQ!

The team of Health Sciences librarians is pleased to announce the launch of the new Health Sciences FAQ. We have put together a list of 22 of the most common questions we’ve seen across the various health sciences fields and provided in-depth answers, as well as resources to help you.

Questions cover topics related to knowledge synthesis, including different types of reviews, foreground vs. background questions, the evidence pyramid, searching, medical databases and more! The FAQ is for anyone thinking about or currently undertaking research in the health sciences, including students in the disciplines of medicine, dentistry and nursing. Does it explain the difference between subject headings and keywords? You bet! Does it answer your PICO assignment? No (sorry!), but it does explain PICO and other question formulation frameworks. 

Still have questions? No problem! Feel free to submit a question for our consideration or leave a comment on an already-published post. Remember, for more immediate assistance during the semester, you can chat or text with a librarian from 10 am to 6 pm, Monday through Friday, and from 12 pm to 5 pm on Saturday and Sunday. Find more information about our Ask a Librarian service here