Factors in creating sustainable intersectoral community mobilization for prevention of heart and lung disease
Bibliographic record
Abstract
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.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".