Capitation and enhanced fee-for-service models for primary care reform: a population-based evaluation
Bibliographic record
Abstract
BACKGROUND: Primary care reform in Ontario, Canada, included the initiation of a blended capitation model in 2001-2002 and an enhanced fee-for-service model in 2003. Both models involve patient rostering, incentives for preventive care and requirements for after-hours care. We evaluated practice characteristics and patterns of care under both models. METHODS: Using administrative data, we identified physicians belonging to either the capitation or the enhanced fee-for-service group throughout the period from Sept. 1, 2005, to Aug. 31, 2006, and their enrolled patients. Practices were stratified by location (urban v. rural). We compared the groups in terms of practice characteristics and patterns of care, including comprehensiveness of care, continuity of care, after-hours care, visits to the emergency department and uptake of new patients. RESULTS: Patients in the capitation and enhanced fee-for-service practices had similar demographic characteristics. Patients in capitation practices had lower morbidity and comorbidity indices. Comprehensiveness and continuity of care were similar between the 2 groups. Compared with patients in enhanced fee-for-service practices, those in capitation practices had less after-hours care (adjusted rate ratio [RR] 0.68, 95% confidence interval [CI] 0.61-0.75) and more visits to emergency departments (adjusted RR 1.20, 95% CI 1.15-1.25). Overall, physicians in the capitation group enrolled fewer new patients than did physicians in the enhanced fee-for-service group (37.0 v. 52.0 per physician); the same was true of new graduates (60.3 v. 72.1 per physician). INTERPRETATION: Physicians enrolled in the capitation model had different practice characteristics than those in the enhanced fee-for-service model. These characteristics appeared to be pre-existing and not due to enrolment in a new model. Although the capitation model provides an alternative to fee-for-service practice, its characteristics should be the focus of future policy development and research.
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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 | 0.048 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".