Evaluating the impact of a social medicine course delivered in a local‐global context: A 10‐year multi‐site analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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 it