An umbrella review of intersectoral and multisectoral approaches to health policy
Bibliographic record
Abstract
Despite the widespread acceptance of the need for intersectoral and multisectoral approaches, knowledge around how to support, achieve, and sustain multisectoral action is limited. While there have been studies that seek to collate evidence on multisectoral action with a specific focus (e.g., Health in All Policies [HiAP]), we postulated that successes of working cross-sectorally to achieve health goals with one approach can glean insights and perhaps translate to other approaches which work across sectors (i.e., shared insights across HiAP, Healthy Cities, One Health, and other approaches). Thus, 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 of 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. This umbrella review (i.e., review of reviews) was informed by the PRISMA guidelines for scoping reviews, yielding 10 studies included in this review. Enablers detailed are: (1) systems for liaising and engaged communication; (2) political leadership; (3) shared vision or common goals (win-win strategies); (4) education and access to information; and (5) funding. Barriers detailed were: (1) lack of shared vision across sectors; (2) lack of funding; (3) lack of political leadership; (4) lack of ownership and accountability; and (5) insufficient and unavailable indicators and data. These findings provide a rigorous evidence base for policymakers to inform intersectoral and multisectoral approaches to not only aid in the achievement of goals, such as the Sustainable Development Goals, but to work towards 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 | 0.098 | 0.143 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.050 | 0.052 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".