Integration of chronic disease prevention and management services into primary care (PR1MaC): findings from an embedded qualitative study
Bibliographic record
Abstract
BACKGROUND: The PR1MaC study was conducted to evaluate the integration of Chronic Disease Prevention and Management services into primary care practices and was reported effective. The aim of this study was to further explore the effects of the PR1MaC intervention on patients and their family. METHODS: We conducted a qualitative study embedded in a randomized controlled trial. The trial was implemented in eight primary health care practices in the Saguenay region, Quebec, Canada. The interdisciplinary patient-centred team-based intervention included self-management support and a motivational approach. We conducted focus groups and semi-directed individual interviews with patients, family members and healthcare professionals. RESULTS: Perceived positive effects can be grouped into six major themes: awareness, improved knowledge, improved motivation and empowerment, adoption of healthy behaviours, improvement of health status and improvement of quality of life. On the negative side, some participants reported lack of sustainability of newly acquired benefits in the months following the intervention. CONCLUSIONS: Integrating chronic disease prevention and management services into primary care settings had impacts on patients and their family members. These findings are consistent with findings that were reported in the quantitative study. Further studies should address longterm sustainabilility in terms of benefits for the patients. TRIAL REGISTRATION: ClinicalTrials.gov, no.: NCT01319656 .
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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 | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".