Medical education blueprint: Building a postgraduate social medicine rotation
Bibliographic record
Abstract
Introduction: The physician as health advocate is an important concept in postgraduate medical education and accreditation. However, the integration of non-medical expert competencies into residency training remains challenging. The lack of social medicine curricula in Canadian internal medicine postgraduate training highlights the need for innovation in developing effective health advocacy educational interventions. Methods: Since 2018, the McMaster Social Medicine Rotation has provided experiential learning based on six pillars: inner city health and addictions, chronic illness and disability, Indigenous health, newcomer health, anti-racism in healthcare, and 2SLGBTQ+ and sexual health. Two- or four-week rotations provide residents with exposure to the care of marginalized populations through outpatient clinics and the inpatient Substance Use Service. Results: Over 30 residents have completed this elective rotation to date. Residents’ evaluations indicated a score of 4.79 out of 5 for the overall rotation experience and highlighted clinical experience, level of supervision, and promotion of learning as strengths. Discussion: Strong resident leadership and longitudinal community engagement are vital in building an effective postgraduate Social Medicine Rotation. Beyond didactic teaching, health advocacy education should prioritize experiential and community-based learning.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.122 | 0.043 |
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".