Factors That Impact the Success of Interorganizational Health Promotion Collaborations: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: To explore published empirical literature in order to identify factors that facilitate or inhibit collaborative approaches for health promotion using a scoping review methodology. DATA SOURCE: A comprehensive search of MEDLINE, CINAHL, ScienceDirect, PsycINFO, and Academic Search Complete for articles published between January 2001 and October 2015 was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. STUDY INCLUSION AND EXCLUSION CRITERIA: To be included studies had to: be an original research article, published in English, involve at least 2 organizations in a health promotion partnership, and identify factors contributing to or constraining the success of an established (or prior) partnership. Studies were excluded if they focused on primary care collaboration or organizations jointly lobbying for a cause. DATA EXTRACTION: Data extraction was completed by 2 members of the author team using a summary chart to extract information relevant to the factors that facilitated or constrained collaboration success. DATA SYNTHESIS: NVivo 10 was used to code article content into the thematic categories identified in the data extraction. RESULTS: Twenty-five studies across 8 countries were identified. Several key factors contributed to collaborative effectiveness, including a shared vision, leadership, member characteristics, organizational commitment, available resources, clear roles/responsibilities, trust/clear communication, and engagement of the target population. CONCLUSION: In general, the findings were consistent with previous reviews; however, additional novel themes did emerge.
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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 | 0.050 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".