Cross-Sector Collaboration to Improve Access to Community Services for People Living With Diabetes: Contributions From Actor-Network Theory
Bibliographic record
Abstract
Diabetes is a global public health issue. The Public Health Agency of Canada published a Diabetes Framework 2022 which recommends collaborative work across sectors to mitigate the impact of diabetes on health and quality of life. Since 2020, the INMED-COMMUNITY pathway has been implemented in Laval, Québec developing collaboration between healthcare and community sectors through a participatory action research approach. The aim of this article is to gain a better understanding of the INMED-COMMUNITY pathway implementation process, based on the mobilization of network actor theory. Qualitative analysis of semi-structured interviews conducted from January to March 2023 with 12 participants from 3 different sectors (community, health system, research), were carried out using actor-network theory. The results explored the conditions for effective intersectoral collaboration in a participatory action research approach to implement the INMED-COMMUNITY pathway. These were: (1) contextualization of the project, (2) a consultation approach involving various stakeholders, (3) creation of new partnerships, (4) presence of a project coordinator, and (5) mobilization of stakeholders around a common definition of diabetes. Mediation supported by a project coordinator contributed to the implementation of an intersectoral collaborative health intervention, largely due to early identification of controversies.
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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 | 0.035 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".