Measurement and evaluation practices of factors that contribute to effective health promotion collaboration functioning: A scoping review
Bibliographic record
Abstract
The purpose of this scoping review was to identify promising factors that underpin effective health promotion collaborations, measurement approaches, and evaluation practices. Measurement approaches and evaluation practices employed in 14 English-language articles published between January 2001 and October 2015 were considered. Data extraction included research design, health focus of the collaboration, factors being evaluated, how factors were conceptualized and measured, and outcome measures. Studies were methodologically diverse employing either quantitative methods (n=9), mixed methods (n=4), or qualitative methods (n=1). In total, these 14 studies examined 113 factors, 88 of which were only measured once. Leadership was the most commonly studied factor but was conceptualized differently across studies. Six factors were significantly associated with outcome measures across studies; leadership (n=3), gender (n=2), trust (n=2), length of the collaboration (n=2), budget (n=2) and changes in organizational model (n=2). Since factors were often conceptualized differently, drawing conclusions about their impact on collaborative functioning remains difficult. The use of reliable and validated tools would strengthen evaluation of health promotion collaborations and would support and enhance the effectiveness of collaboration.
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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 | 0.184 | 0.390 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.041 | 0.036 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".