Implementation of an integrated primary care cardiometabolic risk prevention and management network in Montréal: does greater coordination of care with primary care physicians have an impact on health outcomes?
Bibliographic record
Abstract
INTRODUCTION: Chronic disease management requires substantial services integration. A cardiometabolic risk management program inspired by the Chronic Care Model was implemented in Montréal for patients with diabetes or hypertension. One of this study's objectives was to assess the impact of care coordination between the interdisciplinary teams and physicians on patient participation in the program, lifestyle improvements and disease control. METHODS: We obtained data on health outcomes from a register of clinical data, questionnaires completed by patients upon entry into the program and at the 12-month mark, and we drew information on the program's characteristics from the implementation analysis. We conducted multiple regression analyses, controlling for patient sociodemographic and health characteristics, to measure the association between interdisciplinary team coordination with primary care physicians and various health outcomes. RESULTS: A total of 1689 patients took part in the study (60.1% participation rate). Approximately 40% of patients withdrew from the program during the first year. At the 12-month follow-up (n = 992), we observed a significant increase in the proportion of patients achieving the various clinical targets. The perception by the interdisciplinary team of greater care coordination with primary care physicians was associated with increased participation in the program and the achievement of better clinical results. CONCLUSION: Greater coordination of patient services between interdisciplinary teams and primary care physicians translates into benefits for patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".