Impact of a Regional Distributed Medical Education Program on an Underserved Community
Bibliographic record
Abstract
PURPOSE: To describe community leaders' perceptions regarding the impact of a fully distributed undergraduate medical education program on a small, medically underserved host community. METHOD: The authors conducted semistructured interviews in 2007 with 23 community leaders representing, collectively, the education, health, economic, media, and political sectors. They reinterviewed six participants from a pilot study (2005) and recruited new participants using purposeful and snowball sampling. The authors employed analytic induction to organize content thematically, using the sectors as a framework, and they used open coding to identify new themes. The authors reanalyzed transcripts to identify program outcomes (e.g., increased research capacity) and construct a list of quantifiable indicators (e.g., number of grants and publications). RESULTS: Participants reported their perspectives on the current and anticipated impact of the program on education, health services, the economy, media, and politics. Perceptions of impact were overwhelmingly positive (e.g., increased physician recruitment), though some were negative (e.g., strains on health resources). The authors identified new outcomes and confirmed outcomes described in 2005. They identified 16 quantifiable indicators of impact, which they judged to be plausible and measureable. CONCLUSIONS: Participants perceive that the regional undergraduate medical education program in their community has broad, local impacts. Findings suggest that early observed outcomes have been maintained and may be expanding. Results may be applicable to medical education programs with distributed or regional sites in similar rural, remote, and/or underserved regions. The areas of impact, outcomes, and quantifiable indicators identified will be of interest to future researchers and evaluators.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".