Missing the target? Teaching undergraduate medical students to “go upstream” and advocate on social determinants of health
Bibliographic record
Abstract
BACKGROUND: healthcare inequities rather than simply be aware of them. METHODS: Using Metzl and Hansen's structural competency framework, we designed and piloted an intervention for medical students. Participants attended a workshop, presented to a hypothetical political stakeholder, and wrote anopinion editorial piece. Students participated in a focus group that was audio-recorded and transcribed. We coded and analyzed presentations, editorials, and transcripts to develop a thematic analysis. RESULTS: Nine students participated in the workshop. They chose structural interventions and presented potential solutions to structural barriers in written and oral outputs. Students identified a lack of knowledge about health and political systems as a potential barrier to future advocacy work. CONCLUSION: Medical trainees require training in specific advocacy skills such as oral and written communication, however this alone may be insufficient. As future advocates, trainees must also acquire a specific skill set and associated knowledge about health systems and policy to navigate the systems in which they will practice.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".