Priorities for action to improve cardiovascular preventive care of patients with multimorbid conditions in primary care--a participatory action research project
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) prevention in patients with multimorbid conditions is not always optimal in primary care (PC). Interactive collaborative processes involving PC community are recommended to develop new models of care and to successfully reshape clinical practices. OBJECTIVE: To identify challenges and priorities for action in PC to improve CVD prevention among patients with multimorbid conditions. METHODS: Physicians (n = 6), nurses (n = 6), community pharmacists (n = 6), other health professionals (n = 6), patients (n = 6) and family members (n = 6), decision makers (n = 6) and researchers (n = 6) took part in a 1-day workshop. Using the Chronic Care Model (CCM) as a framework, participants in focus groups and nominal groups identified the challenges and priorities for action. RESULTS: Providing appropriate support to lifestyle change in patients and implementing collaborative practices are challenging. Priorities for action relate to three CCM domains: (i) improve the clinical information system by providing computerized tools for interprofessional and interinstitutional communication, (ii) improve the organization of health care and delivery system design by enhancing interprofessional collaboration, especially with nurses and pharmacists, and creating care teams that include a case manager and (iii) improve self-management support by giving patients access to nutritionists, to personalized health care plans including lifestyle recommendations and to other resources (community resources, websites). CONCLUSIONS: To optimize CVD prevention, PC actors recommend focussing mainly on three CCM domains. Electronic medical records, collaborative practices and self-management support are perceived as pivotal aspects of successful PC prevention programme. Developing and implementing such models are challenging and will require the mobilization of the whole PC community.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".