Development of an interprofessional program for cardiovascular prevention in primary care: A participatory research approach
Bibliographic record
Abstract
BACKGROUND: The chronic care model provides a framework for improving the management of chronic diseases. Participatory research could be useful in developing a chronic care model-based program of interventions, but no one has as yet offered a description of precisely how to apply the approach. OBJECTIVES: An innovative, structured, multi-step participatory process was applied to select and develop (1) chronic care model-based interventions program to improve cardiovascular disease prevention that can be adapted to a particular regional context and (2) a set of indicators to monitor its implementation. METHODS: Primary care clinicians (n = 16), administrative staff (n = 2), patients and family members (n = 4), decision makers (n = 5), researchers, and a research coordinator (n = 7) took part in the process. Additional primary care actors (n = 26) validated the program. RESULTS: The program targets multimorbid patients at high or moderate risk of cardiovascular disease with uncontrolled hypertension, dyslipidemia or diabetes. It comprises interprofessional follow-up coordinated by case-management nurses, in which motivated patients are referred in a timely fashion to appropriate clinical and community resources. The program is supported by clinical tools and includes training in motivational interviewing. A set of 89 process and clinical indicators were defined. CONCLUSION: Through a participatory process, a contextualized interventions program to optimize cardiovascular disease prevention and a set of quality indicators to monitor its implementation were developed. Similar approach might be used to develop other health programs in primary care if program developers are open to building on community strengths and priorities.
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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 | 0.117 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".