A Peer-Based Approach to Reducing Stigma and Improving Mental Health Support for Medical Students
Bibliographic record
Abstract
AbstractMedical students experience a tremendous amount of stress during their training, which can have a profound effect on mental wellness. Several medical students at the University of Ottawa have created a peer-based program called Mind the Gap (MtG), which aims to improve mental health support and combat mental health-related stigma within the medical student community. The program consists of monthly meetings that invite students to discuss personal experiences and issues surrounding mental illness. The following article is a commentary outlining the MtG program, including its rationale and goals, and the challenges in implementing this type of program. RésuméLes étudiants en médecine vivent un stress énorme au cours de leur formation, ce qui peut avoir un impact profond sur leur bien-être mental. Plusieurs étudiants en médecine à l’Université d’Ottawa ont mis sur pied un programme appelé « Mind the Gap » (MtG), qui vise à améliorer le soutien en santé mentale et à combattre la stigmatisation liée à la santé mentale dans la communauté médicale étudiante. Le programme est composé de rencontres mensuelles qui permettent aux étudiants de discuter de leurs expériences personnelles et des problèmes liés à la maladie mentale. L’article suivant est un commentaire donnant un aperçu du programme MtG, incluant sa raison d’être et ses buts, et les défis qui surviennent lors de la mise en place d’un tel programme.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".