Intersectoral and multisectoral approaches to health policy: an umbrella review protocol
Bibliographic record
Abstract
BACKGROUND: It is widely recognized that one's health is influenced by a multitude of nonmedical factors, known as the social determinants of health (SDH). The SDH are defined as "the conditions in which people are born, grow, live, work and age, and which are shaped by the distribution of money, power and resources at global, national and local levels". Despite their influence on health, most of the SDH are targeted through government departments and ministries outside of the traditional health sector (e.g. education, housing). As such, the need for intersectoral and multisectoral approaches arises. Intersectoral and multisectoral approaches are thought to be essential to addressing many global health challenges our world faces today and achieving the Sustainable Development Goals. There are various ways of undertaking intersectoral and multisectoral action, but there are three widely recognized approaches (Health in All Policies [HiAP], Healthy Cities, and One Health) that each have a unique focus. However, despite the widespread acceptance of the need for intersectoral and multisectoral approaches, knowledge around how to support, achieve and sustain multisectoral action is limited. The goal of this study is to assemble evidence from systematic approaches to reviewing the literature (e.g. scoping review, systematic review) that collate findings on facilitators/enablers and barriers to implementing various intersectoral and multisectoral approaches to health, to strengthen understanding of how to best implement health policies that work across sectors, whichever they may be. METHODS: An umbrella review (i.e. review of reviews) is to be undertaken to collate findings from the peer-reviewed literature, specifically from Ovid MEDLINE and Scopus databases. This umbrella review protocol was developed following the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P), and study design informed by the PRISMA guidelines for scoping reviews (PRISMA-ScR). DISCUSSION: Countries that employ multisectoral approaches are better able to identify and address issues around poverty, housing and others, by working collaboratively across sectors, with multisectoral action by governments thought to be required to achieve health equity.
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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 | 0.227 | 0.186 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.067 | 0.015 |
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".