Bibliographic record
Abstract
Abstract This chapter critically reviews the findings of two surveys, 2019 and 2021, on medical student wellbeing completed at the Cummings School of Medicine completed at the University of Calgary, Canada. Medical student endorsed a significant level of exhaustion at 74% with 20% of students reporting a diagnosis of a mental health disorder. Most common was attention deficit hyperactivity disorder (ADHD), anxiety, and a depressive disorder. 13% tested Cut, Annoyed, Guilty, and Eye (CAGE) positive and 18% endorsed using non-prescription drugs for mood enhancement. 9% reported using medication for cognitive enhancement. 39% reported previous use of cannabis. The role of mitigating factors in the personal, organizational, and cultural domains are explicated. The authors endorse a paradigm shift from pathogenesis to salutogenesis to enhance wellbeing and better coping strategies and avoid pathologizing which may inadvertently blame, and shame stressed students. Innovations in curriculum and student wellness (SAW) hub supports are outlined. In particular, SAW encourages confidential and timely access to mental health supports through a variety of student resources including workshops on the CaRMS matching process, mentoring, and FOF (Forum on Failure). The promotion of a university culture of liberal science, viewpoint diversity, and truth-seeking is emphasized as essential for the development of critical thinking skills, agency, and student resilience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.509 | 0.235 |
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".