Understanding the challenges of intersectoral action in public health through a case study of early childhood programmes and services
Bibliographic record
Abstract
After two decades of intersectoral public health action, the literature reports considerable ongoing difficulty in achieving this aim. This article analyses two of the challenges of intersectoral action: (1) ensuring convergence among the interests and resources of sectoral actors, and (2) coordinating the multiplicity of sectoral programmes. A case study employing Actor–Network Theory is used to provide an in-depth understanding of the persistence of these problems. In 2008, the Montreal Directorate of Public Health in the province of Quebec, Canada, implemented a vast consultation and mobilization process to address problems highlighted by the Survey of the School Readiness of Montreal Children. The process mobilized regional and local multi-sectoral actors in order to propose solutions. At the local community level, the process resulted in increased coordination leading to intersectoral innovation, while at the regional level it brought about the deployment of additional resources, albeit in sectoral programmes. This study analyses how intersectoral issues raised by the survey have been addressed so as to produce these results. It discusses how the balance between sectoral interests and the common good, as well as between sector autonomy and interdependence, is central to dealing with these two critical challenges.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.028 | 0.026 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".