Community engagement in US and Canadian medical schools
Bibliographic record
Abstract
Adam O Goldstein, Rachel Sobel BearmanDepartment of Family Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USAIntroduction: This study examines the integration of community engagement and community-engaged scholarship at all accredited US and Canadian medical schools in order to better understand and assess their current state of engagement.Methods: A 32-question data abstraction instrument measured the role of community engagement and community-engaged scholarship as represented on the Web sites of all accredited US and Canadian medical schools. The instrument targeted a medical school's mission and vision statements, institutional structure, student and faculty awards and honors, and faculty tenure and promotion guidelines.Results: Medical school Web sites demonstrate little evidence that schools incorporate community engagement in their mission or vision statements or their promotion and tenure guidelines. The majority of medical schools do not include community service terms and/or descriptive language in their mission statements, and only 8.5% of medical schools incorporate community service and engagement as a primary or major criterion in promotion and tenure guidelines.Discussion: This research highlights significant gaps in the integration of community engagement or community-engaged scholarship into medical school mission and vision statements, promotion and tenure guidelines, and service administrative structures.Keywords: medical school, education, community service, mission, tenure, engagement
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".