Modelling of intersectoral resources for people living with obesity: pilot study of an environmental analysis
Bibliographic record
Abstract
Context: Living with obesity is a complex, multifactorial health issue, and the healthcare system contributes only 20% of the factors affecting the health of a person living with obesity (PLO). Interventions dedicated to PLO need to be rethought according to an integrated, person-centred, and intersectoral approach to consider the complexity of living with obesity. Objective: To model the involvement of the different sectors and services mobilized in the follow-up of PLO in a region of Quebec. Study design: Qualitative study. An environmental scan was conducted between September 2023 and January 2024 using a literature review and semi-structured interviews. Setting: Regional County Municipality of Montmagny, semi-rural region (Chaudière-Appalaches), in Quebec, Canada. Study population: PLO, healthcare professionals and community workers. Intervention: A literature review was conducted to identify current and potential intersectoral interventions dedicated to PLO in Canada. This was followed by semi-structured interviews with a citizen partner, a health system manager, and a community worker to document their perspectives and implications and contextualize findings to the study region. Outcome measures: Qualitative data analysis was conducted deductively using the Health system Pineault framework and Network actor theory. Health resources modelling was carried out according to systems modelling principles. Results: Health professionals and community workers did not know, use, or coordinate all sectors of the community that may be involved in PLOs9 health. For social and organizational reasons, PLOs did not use many public health system resources, and private community resources were not coordinated with the public system. Conclusions: This pilot study shows that care and services dedicated to PLO are currently centred on the healthcare system. The resources available in the community are still little known to PLOs and healthcare professionals and are essentially served by the private system.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".