Medical School Characteristics, Policies, and Practices That Support Primary Care Specialty Choice: A Scoping Review of 5 Decades of Research
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The United States, like many other nations, faces a chronic shortage of primary care physicians. The purpose of this scoping review was to synthesize literature describing evidence-based institutional practices and interventions that support medical students' choices of primary care specialties, published in the United States, Canada, Australia, and New Zealand. METHODS: We surveyed peer-reviewed, published research. An experienced medical librarian conducted searches of multiple databases. Articles were selected for inclusion based on explicit criteria. We charted articles by topic, methodology, year of publication, journal, country of origin, and presence or absence of funding. We then scored included articles for quality. Finally, we defined and described six common stages of development of institutional interventions. RESULTS: We reviewed 8,083 articles and identified 199 articles meeting inclusion criteria and 41 related articles. As a group, studies were of low quality, but improved over time. Most were quantitative studies conducted in the United States. Many studies utilized one of four common methodologic approaches: retrospective surveys, studies of programs or curricula, large-scale multi-institution comparisons, and single-institution exemplars. Most studies developed groundwork or examined effectiveness or impact, with few studies of planning or piloting. Few studies examined state or regional workforce outcomes. CONCLUSIONS: Research examining medical school interventions and institutional practices to support primary care specialty choice would benefit from stronger theoretical grounding, greater investment in planning and piloting, consistent use of language, more qualitative methods, and innovative approaches. Robust funding mechanisms are needed to advance these goals.
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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 | 0.031 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".