Using participatory research to develop inter-sectorial collaboration between primary care teams and community organizations
Bibliographic record
Abstract
Introduction: Diabetes is a global public health problem. In 2022, the Public Health Agency of Canada published a framework to improve diabetes care. A key recommendation was collaborative action from diverse sectors Objective: To describe 1) the process of implementing an innovative community care pathway through intersectoral collaboration to improve the empowerment of people with diabetes and 2) the perception of stakeholders involved in implementing this pathway. Type of study: Participatory research. Location: two family medicine groups (FMGs) of an integrated health and social services center (CISSS) located on the outskirts of Montreal (Canada). Participants: patient partners, professionals from two FMGs and the diabetes center, managers, community organizers, head of a community organization and researchers. Intervention: 1) Co-creation and implementation of an innovative community care pathway to support the empowerment of people living with diabetes; 2) Evaluation of the perception of the partners involved to identify their acceptability of this intervention. Main evaluation parameters: A central committee and several subcommittees involving participants were created and met regularly for 12 months. Meetings were recorded, and field notes were taken to document the participatory research process. 12 Individual interviews were conducted with core committee members to assess their perceptions. Qualitative data analysis was conducted deductively using NVIVO and Bilodeau’s framework. Results: The activation of partnership links was facilitated by the creation of tools to refer patients from the clinical setting to the community setting and to support the empowerment of people living with diabetes. The design of new partnership links between the clinical departments and the community was facilitated by the holding of several regular formal and informal meetings organized by the project team, by the presence of a project coordinator who facilitated links between the players, by the improvement of communication between the players, by the use of a common language, by the presence of the patient partners at all stages of the project, which made the meetings more relevant, and by a common understanding of diabetes Conclusion: Intersectoral collaboration optimizes implementing an innovative approach to care that focuses on the person-centred needs and the determinants of health. A structured process and favourable conditions are needed to operationalize it
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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 | 0.249 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".