Identifying characteristics of intersectoral health interventions between the primary care and community settings for people living with obesity: an environmental scan protocol
Bibliographic record
Abstract
INTRODUCTION: Obesity, a complex chronic disease, is on the rise, leading to increased mortality, morbidity and societal challenges. This study explores intersectoral interventions focusing on the needs of people living with obesity (PLO). METHODS AND ANALYSIS: An environmental scan of the published and unpublished literature will be conducted using Medline, Embase, Cumulated Index in Nursing and Allied Health Literature and specialised websites. To be included, citations must describe or evaluate an intersectoral intervention for PLO developed in primary care or community settings. Title and abstract, full-text screening and extraction will be completed by two independent reviewers. Discrepancies will be resolved through consensus. Data such as study and intervention characteristics will be extracted using a customised extraction template on Covidence and synthesised in a table. Findings from this study will guide intervention design and enhance intersectoral collaboration in primary care and community settings. A multidisciplinary group, including clinicians and two patient partners, will be consulted throughout the process. Despite the challenges of defining intersectoral collaboration and limited data on obesity as a chronic disease, this study is foundational for developing effective intersectoral interventions for PLO. ETHICS AND DISSEMINATION: Ethics approval is not required. Findings will be disseminated through presentations at relevant conferences and other knowledge translation activities and will be published in a peer-reviewed journal.
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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 | 0.068 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
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".