Evaluating the impact of a social medicine course delivered in a local‐global context: A 10‐year multi‐site analysis
Bibliographic record
Abstract
Abstract Amidst the COVID‐19 pandemic and social uprisings demanding social and racial equity worldwide, there is an increasing demand for health justice training for health workers. However, there are scant evidence‐based assessments of the impact of such courses. Between 2010 and 2020, SocMed—a 501(c)3 non‐profit social justice organization—offered two distinct courses about health equity, the social determinants of health, and social medicine to health workers through the University of Minnesota in Minneapolis, Minnesota and Saint Mary Hospital Lacor in Gulu, Uganda. This study assesses the immediate impact of the SocMed curriculum on participants measured by a pre‐course and post‐course survey. In Minnesota, paired pre‐course and post‐course survey responses (mean n = 69; SD = 23) spanned years 2016–2019, while Uganda paired pre‐course and post‐course survey responses (mean n = 64; SD = 21) spanned years 2012–2013 and 2017–2019. Findings indicate that the course improved participants’ knowledge in all 24 of the topics in the Minnesota course and 42 of 44 topics in the Uganda course (significant at p < 0.05).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".