Examining Intersectoral Action as an Approach to Implementing Multistakeholder Collaborations to Achieve the Sustainable Development Goals
Bibliographic record
Abstract
Objectives: The Sustainable Development Goals (SDGs) re-orient action towards improving the social and ecological determinants of health and equity. SDG 17 calls for enhanced policy and institutional coherence and strong multi-stakeholder partnerships. Intersectoral action (IA) has a promising history in public health, including health promotion and global health. Some experts see IA as crucial to the SDGs. Yet less is known about how IA is conceptualized and what promising models exist with relevance to the SDGs. We sought to investigate how IA is understood conceptually and empirically. Methods: We conducted a narrative review of global public health and political science literatures and grey literature on the SDGs to identify theoretical models, case studies and reviews of IA research. Results: Multiple competing conceptualizations of IA exist. Research has focused on case studies in high-income countries. More conceptual clarity, analyses of applications in LMICs, and explorations of political and institutional factors affecting IA are needed, as is attention to power dynamics between sectors. Conclusion: IA is required to collaborate on the SDGs and address equity. New models for successful implementation merit exploration.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".