Learning from intersectoral action beyond health: a meta-narrative review
Bibliographic record
Abstract
Intersectoral action (ISA) is considered pivotal for achieving health and societal goals but remains difficult to achieve as it requires complex efforts, resources and coordinated responses from multiple sectors and organizations. While ISA in health is often desired, its potential can be better informed by the advanced theory-building and empirical application in real-world contexts from political science, public administration and environmental sciences. Considering the importance and the associated challenges in achieving ISA, we have conducted a meta-narrative review, in the research domains of political science, public administration, environmental and health. The review aims to identify theory, theoretical concepts and empirical applications of ISA in these identified research traditions and draw learning for health. Using the multidisciplinary database of SCOPUS from 1996 to 2017, 5535 records were identified, 155 full-text articles were reviewed and 57 papers met our final inclusion criteria. In our findings, we trace the theoretical roots of ISA across all research domains, describing the main focus and motivation to pursue collaborative work. The literature synthesis is organized around the following: implementation instruments, formal mechanisms and informal networks, enabling institutional environments involving the interplay of hardware (i.e. resources, management systems, structures) and software (more specifically the realms of ideas, values, power); and the important role of leaders who can work across boundaries in promoting ISA, political mobilization and the essential role of hybrid accountability mechanisms. Overall, our review reaffirms affirms that ISA has both technical and political dimensions. In addition to technical concerns for strengthening capacities and providing support instruments and mechanisms, future research must carefully consider power and inter-organizational dynamics in order to develop a more fulsome understanding and improve the implementation of intersectoral initiatives, as well as to ensure their sustainability. This also shows the need for continued attention to emergent knowledge bases across different research domains including health.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".