Explaining primary care physicians’ decision to quit patient‐centered medical homes: Evidence from Quebec, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the factors explaining primary care physicians' (PCPs) decision to leave patient-centered medical homes (PCMHs). DATA SOURCES: Five-year longitudinal data on all the 906 PCPs who joined a PCMH in the Canadian province of Quebec, known there as a Family Medicine Group. STUDY DESIGN: We use fixed-effects and random-effects logit models, with a variety of regression specifications and various subsamples. In addition to these models, we examine the robustness of our results using survival analysis, one lag in the regressions and focusing on a matched sample of quitters and stayers. DATA COLLECTION/EXTRACTION METHODS: We extract information from Quebec's universal health insurer billing data on all the PCPs who joined a PCMH between 2003 and 2005, supplemented by information on their elderly and chronically ill patients. PRINCIPAL FINDINGS: About 17 percent of PCPs leave PCMHs within 5 years of follow-up. Physicians' demographics have little influence. However, those with more complex patients and higher revenues are less likely to leave the medical homes. These findings are robust across a variety of specifications. CONCLUSION: As expected, higher revenue favors retention. Importantly, our results suggest that PCMH may provide appropriate support to physicians dealing with complex patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".