MétaCan
Menu
Back to cohort
Record W2137626701 · doi:10.1093/heapro/18.2.135

Factors in creating sustainable intersectoral community mobilization for prevention of heart and lung disease

2003· article· en· W2137626701 on OpenAlexaffabout
Josée Bourdages, Lyne Sauvageau, Céline Lepage

Bibliographic record

VenueHealth Promotion International · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMinistère de la Santé et des Services Sociaux (Québec)Cegep de Sainte Foy
Fundersnot available
KeywordsMobilizationAutonomyGeneral partnershipCommunity mobilizationGovernment (linguistics)Public relationsCommunity organizationPolitical scienceResource mobilizationBusinessPublic administrationPoliticsSocial movement

Abstract

fetched live from OpenAlex

This paper describes factors facilitating and working against successful community mobilization in the implementation of an integrated prevention programme for cardiovascular disease and lung cancer in four community settings in Québec, Canada. Implementation evaluation data from several sources showed that over the 3-year period, mobilization was partly achieved in all four communities, although the degree of success varied. The data support those of previous studies showing that several factors are key to effective intersectoral community mobilization: (i) involvement of concerned and influential community members with a commitment to shared goals and a visible community focus; (ii) formation of multi-organization systems among appropriate organizations, recognizing their strengths, resources and competencies, and preserving both their autonomy and interdependence with an appreciation of divergent perspectives; (iii) development of decision-making mechanisms through the setting up of formal structural arrangements to facilitate decisions with clear leadership; (iv) clear definition of objectives, tasks, roles and responsibilities; and (v) official support and legitimization from participating agencies, government authorities, and organizations with adequate resources devoted to partnership building. This study also replicated a number of barriers to the creation of sustainable intersectoral community mobilization, notably the potentially destructive role of power conflicts among the key institutional partners.

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.515
GPT teacher head0.651
Teacher spread0.136 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueHealth Promotion InternationalSame topicHealth Policy Implementation ScienceFrench-language works237,207