Institutional Characteristics Influencing Medical Student Selection of Primary Care Careers: A Narrative Review and Synthesis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is an ongoing shortage of primary care physicians in the United States. Medical schools are under pressure to address this threat to the nation's health by producing more primary care graduates, including family physicians. Our objective was to identify institutional characteristics associated with more medical students choosing primary care. METHODS: We conducted a systematic literature review with narrative synthesis to identify medical school characteristics associated with increased numbers or proportions of primary care graduates. We included peer-reviewed, published research from the United States, Canada, Australia, and New Zealand. The existing literature on characteristics, including institutional geography, funding and governance, mission, and research emphasis, was analyzed and synthesized into summary statements. RESULTS: Ensuring a strong standing of the specialty of family medicine and creating an atmosphere of acceptance of the pursuit of primary care as a career are likely to increase an institution's percentage of medical students entering primary care. Training on regional campuses or providing primary care experiences in rural settings also correlates with a larger percentage of graduates entering primary care. A research-intensive culture is inversely correlated with primary care physician production among private, but not public, institutions. The literature on institutional financial incentives is not of high enough quality to make a firm statement about influence on specialty choice. CONCLUSIONS: To produce more primary care providers, medical schools must create an environment where primary care is supported as a career choice. Medical schools should also consider educational models that incorporate regional campuses or rural educational settings.
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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 | 0.017 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".