Students of Change: Health Policy in Action
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Health policy is more impactful for public health than many other strategies as it can improve health outcomes for an entire population. Yet in the "see one, do one, teach one" environment of medical school, most students never get past the "see one" stage in learning about the powerful tools of health policy and advocacy. The University of New Mexico School of Medicine mandates health policy and advocacy education for all medical students during their family medicine clerkship rotation. The aim of this project is to describe a unique health policy and advocacy course within a family medicine clerkship. METHODS: We analyzed policy briefs from 265 third-year medical students from April 2016 through April 2019. Each brief is categorized by the level of change targeted for policy reform: national, state, city, or university/school. Implemented policies are described. RESULTS: Slightly less than one-third of the policies (30%) relate to education, 36% advocate for health system change by addressing cost, access, or quality issues, and 34% focus on public health issues. Fourteen policies have been initiated or successfully enacted. CONCLUSIONS: This curriculum gives each medical student a health policy tool kit with immediate opportunities to test their skills, learn from health policy and advocacy experts, and in some cases, implement health policies while still in medical school. A 1-week family medicine policy course can have impact beyond the classroom even during medical school, and other schools should consider this as a tool to increase the impact of their graduates.
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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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 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".