Ontario primary care models: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Between 2001 and 2006, the Ontario government introduced a menu of new primary care models, with elements such as patient enrolment and minimum group sizes, and various combinations of fee-for-service, capitation, pay-for-performance and salary. From the statistical perspective of physicians, as opposed to patients, we looked at the distribution of physician characteristics, group size and patient visit patterns across models to describe primary care practice in Ontario. METHODS: Using administrative data for fiscal year 2010/11 containing information on physician characteristics, patient rostering status, patient visits and other practice information, we described similarities and differences across primary care models. RESULTS: Our sample included 11 626 family physicians. Compared with physicians in the new primary care models, physicians in fee-for-service models are much more likely to work part-time and many, particularly younger and female physicians, do not work in full-year full-scope practices. Among the new primary care models, physicians in capitated models are slightly younger, are less likely to be an international medical graduate, work in smaller physician teams and do not practice in urban areas. On average, physicians saw and rostered 1888 patients. Although there is still substantial variation within each model, fee-for-service physicians saw the fewest patients; physicians in capitated models saw somewhat more, and those in the noncapitated models saw the most patients. INTERPRETATION: Practice and physician characteristics vary systematically across models. A high percentage of rostered patients see physicians outside the group with which they are rostered. Group-based primary care models may not have a large impact on group integration and continuity in the provision of primary care services.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".