Early generalist placements are associated with family medicine career choice: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Many developed countries have reported shortages of Primary Care (PC) physicians. The lack of a regular primary physician is associated with inferior health outcomes. One strategy to address this shortage is to increase the proportion of medical students selecting a Family Medicine (FM) or PC career. The purpose of this systematic review is to identify whether pre-clerkship general practice placements increase students' interest in, and selection of FM or PC residencies. METHODS: Three databases (PubMed, Embase, Web of Science) searched for interventional studies of pre-clerkship generalist placements in medical school. Pooled statistical analysis and meta-analysis were performed, along with narrative summaries when possible. Intervention participants (IP) were compared to controls matched (MC) for baseline interest in FM and an unmatched sample (UC) of contemporary students. FINDINGS: A 11 studies were identified including a total of 5430 students (2428 intervention participants and 3002 controls). IPs were more likely to match to FM than both MC (Risk Ratio: 1.62 [95% CI: 1.03-2.55]) and UC (RR: 2.04 [1.46-2.86]). Participation in long interventions (4-11 weeks) matched to FM at higher rates than short (25-40 hours) interventions (RR: 3.15 [2.28-4.35]). The percentage of students with FM/PC as their top specialty of interest increased after the placements (mean difference: +12.8%, n = 586). CONCLUSIONS: Medical students who participated in pre-clerkship general practice placements were more likely to match to a FM residency. Longer pre-clerkship placements had a stronger association with FM specialty choice. The implementation of longitudinal block generalist placements in the pre-clerkship years is one strategy for increasing interest in generalist careers. LEVEL OF EVIDENCE: IV, systematic review of level III and IV studies.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".