The Impact of Family Medicine Interest Groups and Student-Run Free Clinics on Primary Care Career Choice: A Narrative Synthesis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Student-directed activities such as family medicine interest groups (FMIG) and student-run free clinics (SRFC) have been examined to discover their impact on entry into family medicine and primary care. The objective of this review was to synthesize study results to better incorporate and optimize these activities to support family medicine and primary care choice. METHODS: We conducted a comprehensive literature search using PubMed, Scopus, and CINAHL to identify all English-language research articles on FMIG and SRFC. We examined how participation relates to entry into family medicine and primary care specialties. Exclusion criteria were nonresearch articles, review articles, and research conducted outside the United States, Canada, Australia, and New Zealand. We used a 16-point quality rubric to evaluate 18 (11 FMIG, seven SRFC) articles that met our criteria. RESULTS: Of the nine articles that examined whether FMIG participation impacted entry into family medicine, five papers noted a positive relationship, one paper noted unclear correlation, and three papers noted that FMIG did not impact entry into family medicine. Of the seven articles about SRFC, only one showed a positive relationship between SRFC activity and entry into primary care. CONCLUSIONS: Larger-scale and higher quality studies are necessary to determine the impact of FMIG and SRFC on entry into family medicine and primary care. However, available evidence supports that FMIG participation is positively associated with family medicine career choice. In contrast, SRFC participation is not clearly associated with primary care career choice.
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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 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".