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Record W2669992543 · doi:10.1080/09581596.2017.1343934

Understanding the challenges of intersectoral action in public health through a case study of early childhood programmes and services

2017· article· en· W2669992543 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCritical Public Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsMinistry of Health and Social ServicesUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsAutonomySoftware deploymentPolitical sciencePublic healthProcess (computing)Economic growthRegional scienceBusinessSociologyMedicineEconomicsEngineeringNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.622
GPT teacher head0.540
Teacher spread0.082 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it