Are Canadian medicine librarians directly supporting medical student health and wellness? A nation-wide survey
Bibliographic record
Abstract
Introduction: Students in Undergraduate Medical Education (UGME/UME) programs face a variety of stressors that can affect well-being. To address this, the Committee on Accreditation of Canadian Medical Schools (CACMS) mandates that medical schools offer support and programming that promotes student well-being. Academic librarians are accustomed to providing outreach that meets their faculties' needs. Therefore, the goal of this study was to explore if Canadian undergraduate medical education librarians are supporting medical student wellness at their medical schools, and how. Methods: A bilingual, electronic survey containing multiple choice and open-ended questions was distributed across two Canadian health sciences library listservs during the summer of 2020. Librarians supporting UGME/UME programs now or within the last three years were invited to participate. Results: 22 Responses were received, and 17 complete datasets were included in the final results. The majority of respondents have encountered a medical student in distress (n=10) and have adjusted their teaching style or materials to help reduce stress in medical students (n=9). Other initiatives such as resource purchasing, wellness-themed displays, planning wellness-themed events and spaces, and partnerships on campus in support of medical student wellness were less common. Discussion: The data in this study provides evidence that Canadian undergraduate medical education librarians are mindful of medical student well-being, and are taking steps to provide relevant support to this learner group. Librarians could adopt similar initiatives at their libraries to show support for learner wellness, and enhance their programs' accreditation efforts in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".