Family Medicine Residency Training in Community Health Centers: A National Survey
Bibliographic record
Abstract
PURPOSE: For more than 25 years, family medicine residencies (FMRs) have worked with community health centers (CHCs) to train family physicians. In light of the long history and current policy focus on this training model, the authors sought to evaluate comprehensively the scope and extent of family physician training occurring in CHCs. METHOD: The authors conducted a cross-sectional survey of 439 U.S. FMR directors in 2007. FMR directors were asked to provide information regarding the number, type, location, and length of any CHC training affiliations and to rate their satisfaction with such affiliations. RESULTS: Of 354 respondents (80% response rate), 83 FMRs (23.4%) provided some type of CHC training experience; 32 (9%) had their main residency continuity training site in a CHC. Respondents reported that 10.5% (788) of family medicine residents were trained in a CHC continuity clinic. The average length of affiliation was 10.2 years. Residency directors reported high satisfaction with CHC training affiliations. CONCLUSIONS: Almost one-quarter of FMRs in 2007 provided some training in CHCs. However, the proportion of residencies providing continuity training in CHCs--the type of training associated with enhanced recruitment and retention of family medicine graduates in underserved areas--was limited and relatively unchanged since 1992.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".