Characteristics of primary care practices associated with high quality of care
Bibliographic record
Abstract
BACKGROUND: No primary practice care model has been shown to be superior in achieving high-quality primary care. We aimed to identify the organizational characteristics of primary care practices that provide high-quality primary care. METHODS: We performed a cross-sectional observational study involving a stratified random sample of 37 primary care practices from 3 regions of Quebec. We recruited 1457 patients who had 1 of 2 chronic care conditions or 1 of 6 episodic care conditions. The main outcome was the overall technical quality score. We measured organizational characteristics by use of a validated questionnaire and the Team Climate Inventory. Statistical analyses were based on multilevel regression modelling. RESULTS: The following characteristics were strongly associated with overall technical quality of care score: physician remuneration method (27.0; 95% confidence interval [CI] 19.0-35.0), extent of sharing of administrative resources (7.6; 95% CI 0.8-14.4), presence of allied health professionals (15.3; 95% CI 5.4-25.2) and/or specialist physicians (19.6; 95% CI 8.3-30.9), the presence of mechanisms for maintaining or evaluating competence (7.7; 95% CI 3.0-12.4) and average organizational access to the practice (4.9; 95% CI 2.6-7.2). The number of physicians (1.2; 95% CI 0.6-1.8) and the average Team Climate Inventory score (1.3; 95% CI 0.1-2.5) were modestly associated with high-quality care. INTERPRETATION: We identified a common set of organizational characteristics associated with high-quality primary care. Many of these characteristics are amenable to change through practice-level organizational changes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".