The Comparison of Models of Primary Care in Ontario (COMP-PC) study: methodology of a multifaceted cross-sectional practice-based study.
Bibliographic record
Abstract
BACKGROUND: Many industrialized nations have initiated reforms in the organization and delivery of primary care. In Ontario, Canada, salaried and capitation models have been introduced in an attempt to address the deficiencies of the traditional fee-for-service model. The Ontario setting therefore provides an opportunity to compare these funding models within a region that is largely homogeneous with respect to other factors that influence care delivery. We sought to compare the performance of the models across a broad array of dimensions and to understand the underlying practice factors associated with superior performance. We report on the methodology grounding this work. METHODS: Between 2004 and 2006 we conducted a cross-sectional mixed-methods study of the fee-for-service model, including family health groups, family health networks, community health centres and health service organizations. The study was guided by a conceptual framework for primary care organizations. Performance across a large number of primary care attributes was evaluated through surveys and chart abstractions. Nested case studies generated qualitative provider and patient data from 2 sites per model along with insights from key informants and policy-makers familiar with all models. RESULTS: The study recruited 137 practices. We conducted 363 provider surveys and 5361 patient surveys, and we performed 4108 chart audits. We also conducted interviews with 40 family physicians, 6 nurse practitioners, 24 patients and 8 decision-makers. The practice recruitment rate was 45%; it was lowest in fee-for-service practices (23%) and in family health networks (37%). A comparison with all Ontario practices in these models using health administrative data demonstrated that our sample was adequately representative. The patient participation (82%) and survey scale completion (93%) rates were high. CONCLUSIONS: This article details our approach to performing a comprehensive evaluation of primary care models and may be a useful resource for researchers interested in primary care evaluation.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".