Family-centred care delivery: comparing models of primary care service delivery in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To determine whether models of primary care service delivery differ in their provision of family-centred care (FCC) and to identify practice characteristics associated with FCC. DESIGN: Cross-sectional study. SETTING: Primary care practices in Ontario (ie, 35 salaried community health centres, 35 fee-for-service practices, 32 capitation-based health service organizations, and 35 blended remuneration family health networks) that belong to 4 models of primary care service delivery. PARTICIPANTS: A total of 137 practices, 363 providers, and 5144 patients. MAIN OUTCOME MEASURES: Measures of FCC in patient and provider surveys were based on the Primary Care Assessment Tool. Statistical analyses were conducted using linear mixed regression models and generalized estimating equations. RESULTS: Patient-reported FCC scores were high and did not vary significantly by primary care model. Larger panel size in a practice was associated with lower odds of patients reporting FCC. Provider-reported FCC scores were significantly higher in community health centres than in family health networks (P = .035). A larger number of nurse practitioners and clinical services on-site were both associated with higher FCC scores, while scores decreased as the number of family physicians in a practice increased and if practices were more rural. CONCLUSION: Based on provider and patient reports, primary care reform strategies that encourage larger practices and more patients per family physician might compromise the provision of FCC, while strategies that encourage multidisciplinary practices and a range of services might increase FCC.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".