Mental health night: A peer-led initiative
Bibliographic record
Abstract
We describe a peer-led mental health (MH) workshop that was held at the Michael G. DeGroote School of Medicine (Niagara Regional Campus) in collaboration with Student Affairs. Workshop aims included facilitating discussions among peers and engaging in case-based learning about MH experiences in medical school. Post-workshop, attendees reported increased comfort in talking to classmates about personal MH, recognizing MH crises, and asking for help from peers. We believe that engaging medical learners in MH discussions early on in medical education is critical, and that peer-led workshops may aid in decreasing future MH difficulties and burnout. Due to the low-cost of holding peer-led workshops, this event could be easily replicated at other training sites.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".