Managing Chronic Disease in Ontario Primary Care: The Impact of Organizational Factors
Bibliographic record
Abstract
PURPOSE: New approaches to chronic disease management emphasize the need to improve the delivery of primary care services to meet the needs of chronically ill patients. This study (1) assessed whether chronic disease management differed among 4 models of primary health care delivery and (2) identified which practice organizational factors were independently associated with high-quality care. METHODS: We undertook a cross-sectional survey with nested qualitative case studies (2 practices per model) in 137 randomly selected primary care practices from 4 delivery models in Ontario Canada: fee for service, capitation, blended payment, and community health centers (CHCs). Practice and clinician surveys were based on the Primary Care Assessment Tool. A chart audit assessed evidence-based care delivery for patients with diabetes, congestive heart failure, and coronary artery disease. Intermediate outcomes were calculated for patients with diabetes and hypertension. Multiple linear regression identified those organizational factors independently associated with chronic disease management. RESULTS: Chronic disease management was superior in CHCs. Clinicians in CHCs found it easier than those in the other models to promote high-quality care through longer consultations and interprofessional collaboration. Across the whole sample and independent of model, high-quality chronic disease management was associated with the presence of a nurse-practitioner. It was also associated with lower patient-family physician ratios and when practices had 4 or fewer full-time-equivalent family physicians. CONCLUSIONS: The study adds to the literature supporting the value of nurse-practitioners within primary care teams and validates the contributions of Ontario's CHCs. Our observation that quality of care decreased in larger, busier practices suggests that moves toward larger practices and greater patient-physician ratios may have unanticipated negative effects on processes of care quality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".