Side-by-Side: A One-on-One Peer Support Program for Medical Students
Bibliographic record
Abstract
PROBLEM: Medical students experience high levels of burnout and face barriers to accessing support services. However, few studies have considered the feasibility and/or effectiveness of one-on-one peer support programs for medical students. This report aims to describe the development and implementation of such a program, the Side-by-Side Peer Support Program, at the University of Ottawa (August 2018-June 2020). APPROACH: Thirty-five medical students enrolled at the University of Ottawa Faculty of Medicine were selected to participate in a training course aimed at developing the skills necessary to provide one-on-one support to their peers. The main responsibilities of peer supporters were to reach out to classmates, particularly those displaying changes in their usual behavior that might be indicative of mental illness, to provide basic counseling, and to refer at-risk students to professional services. Peer supporters offered weekly hours during which classmates could contact them for support. Information on interactions between students and peer supporters was recorded in an electronic database. An end-of-year survey collected information on barriers to seeking help perceived by medical students. OUTCOMES: A total of 303 interactions were recorded. Interactions took place in various formats, including in-person, via telephone or video call, or via texting or online messaging. Interactions were initiated by both students and peer supporters. Survey respondents identified more barriers to seeking help from Faculty of Medicine services than Side-by-Side, including fear of impact on career (22.2% vs 2.5%; P < .01) and belief that the services would not be helpful (42.0% vs 23.5%; P = .02). NEXT STEPS: The authors plan to quantify well-being through academic engagement metrics as well as mental health outcome metrics in future studies. Future studies should also consider whether peer support increases help-seeking behaviors and/or the use of professional services.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".