Collaborating With Service Users to Select Psychiatry Residents Committed to Health Equity and Social Justice
Bibliographic record
Abstract
PROBLEM: Advocates have called for health services to be delivered equitably to all. Academic psychiatry must play a role in this work, given its history of creating and perpetuating the marginalization of people experiencing mental health issues. While medical educators have started teaching concepts such as structural competency and cultural safety, careful consideration of who enters the medical workforce and what values they bring is also important. APPROACH: The authors report on the first 5 years (2016-2021) of a collaboration with individuals who have used mental health or addiction services or identify as having lived experiences of mental health and/or substance use issues (i.e., service users) to select residents to the general adult psychiatry residency program at the University of Toronto who are committed to working toward health equity and social justice and who bring diverse personal, academic, and community-based experiences. Starting in 2016, a working group of service users and faculty iteratively refined the selection process to add personal letter and interview day writing sample prompts centered on social justice and advocacy. OUTCOMES: The working group, coled by service users since 2019, defined the problem (lack of attention to health equity and social justice in resident selection) and codesigned the solution by revising writing prompts used in the selection process and their assessment rubrics to emphasize these missing areas. Further, service users directly participated in the implementation by reviewing candidates' personal letters and interview day writing samples alongside faculty and residents. This work serves as an example of meaningful service user engagement in action. NEXT STEPS: To ensure the needs of service users are prioritized, future work must aim for long-term institutional commitment to strengthen service user involvement and power sharing with service user communities in resident selection and at other points along the medical education pathway.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".