Factors Associated with Family Physicians Providing High Continuity and Comprehensive Care
Bibliographic record
Abstract
Context While continuity and comprehensiveness have long been considered core tenets of primary care, little is known about the factors associated with physicians who excel in delivering both. Objectives 1) Examine variations in continuity and comprehensiveness scores across individual, practice and geographic levels 2) Identify factors associated with scoring high in continuity, comprehensiveness, or both. Study design Observational cohort study Dataset Medicare Fee-For-Service claims supplemented with administrative data from the American Board of Family Medicine. Sample Board-certified family physicians who saw 30 or more Medicare patients in 2016. Outcomes Physician continuity and comprehensiveness scores. Using the 75th percentile score as a separator, we identified physicians with 1) high (75th percentile or higher) continuity scores, 2) high comprehensiveness scores, and 3) high scores on both measures. Analysis We calculated the mean continuity and comprehensiveness scores by factors at individual level (physician age group, gender, race), practice level (practice settings, patient volume, practice size), and geographic level (practice region and rurality). We then modeled the odds of a physician achieving high continuity scores, high comprehensiveness score, or high scores on both in separate logistic regression models. Results In a national sample of 49,752 family physicians, the mean score was 0.58 for continuity of care and 45 for comprehensiveness, with score variations more notable in continuity. More than a quarter of the physicians scored high on either continuity or comprehensiveness, but only 9.1% scored high on both. Having a panel of 200 or more Medicare patients was associated with the largest increase in physicians’ odds of scoring high on both. The intraclass correlation coefficients offered evidence of health systems’ influence over the continuity and comprehensiveness of care delivered by affiliated physicians. Conclusion A small fraction of family physicians achieved high continuity and comprehensiveness concurrently. Significant variation—especially in continuity—suggests opportunities for targeted improvement. The strong influence of large patient panels raises equity concerns for those with smaller panels aiming to meet core primary care standards. Health systems’ support may improve both continuity and comprehensiveness.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".