Residency Program Characteristics and Individual Physician Practice Characteristics Associated With Family Physician Scope of Practice
Bibliographic record
Abstract
PURPOSE: A family physician's ability to provide continuous, comprehensive care begins in residency. Previous studies show that patterns developed during residency may be imprinted upon physicians, guiding future practice. The objective was to determine family medicine residency characteristics associated with graduates' scope of practice (SCoP). METHOD: The authors used (1) residency program data from the 2012 Accreditation Council for Graduate Medicine Education Accreditation Data System and (2) self-reported data supplied by family physicians when they registered for the first recertification examination with the American Board of Family Medicine (2013-2016)-7 to 10 years after completing residency. The authors used linear regression analyses to examine the relationship between individual physician SCoP (measured by the SCoP for primary care [SP4PC] score [scale of 0-30; low = small scope]) and individual, practice, and residency program characteristics. RESULTS: The authors sampled 8,261 physicians from 423 residencies. The average SP4PC score was 15.4 (standard deviation, 3.2). Models showed that SCoP broadened with increasing rurality. Physicians from unopposed (single) programs had higher SCoP (0.26 increase in SP4PC); those from major teaching hospitals had lower SCoP (0.18 decrease in SP4PC). CONCLUSIONS: Residency program characteristics may influence family physicians' SCoP, although less than individual characteristics do. Broad SCoP may imply more comprehensive care, which is the foundation of a strong primary care system to increase quality, decrease cost, and reduce physician burnout. Some residency program characteristics can be altered so that programs graduate physicians with broader SCoP, thereby meeting patient needs and improving the health system.
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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 | 0.001 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".