Intersectoral health interventions to improve the well-being of people living with type 2 diabetes: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Intersectoral collaboration is a collaborative approach between the health sectors and other sectors to address the interdependent nature of the social determinants of health associated with chronic diseases such as diabetes. This scoping review aims to identify intersectoral health interventions implemented in primary care and community settings to improve the well-being and health of people living with type 2 diabetes. METHODS AND ANALYSIS: methodological enhancement. MEDLINE, Embase, CINAHL, grey literature and the reference list of key studies will be searched to identify any study, published between 2000 and 2023, related to the concepts of intersectorality, diabetes and primary/community care. Two reviewers will independently screen all titles/abstracts, full-text studies and grey literature for inclusion and extract data. Eligible interventions will be classified by sector of action proposed by the Social Determinants of Health Map and the conceptual framework for people-centred and integrated health services and further sorted according to the actors involved. This work started in September 2023 and will take approximately 10 months to be completed. ETHICS AND DISSEMINATION: This review does not require ethical approval. The results will be disseminated through a peer-reviewed publication and presentations to stakeholders.
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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 | 0.119 | 0.087 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.013 |
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".