A city-wide health promotion programme evaluation using EQUIHP: Jerusalem Community-Academic Partnership (J-CAP)
Bibliographic record
Abstract
BACKGROUND: While health promotion initiatives are common, too little is known about their quality, impact and sustainability. Fragmentation between sectors exists and programme evaluation initiatives lack consistency, making comparison of outcomes challenging. METHODS: We used a 'snowball' methodology to detect health promotion programmes (HPPs) in the Municipality of Jerusalem, excluding those in schools. The European Quality Instrument for Health Promotion (EQUIHP) was adapted and used to examine programme quality. The tool was pre-tested among stakeholders, and translated into Hebrew and Arabic between March and December 2017. Trained research assistants collected information on four domains using in-person interviews: (i) compliance with international principles of HPPs, (ii) development and implementation, (iii) project management and (iv) sustainability of programmes. RESULTS: Overall, 93 programmes, including 33 670 participants, were ascertained and evaluated. The majority of HPPs (54.8%) addressed nutrition and physical activity, with 58.1% targeting the non-orthodox Jewish population and 68.8% aimed at both sexes. Cronbach's alpha scores were 0.968 for the entire EQUIHP tool and 0.802, 0.959, 0.918 and 0.718 for the subdomains of Framework, Project Development, Project Management and Sustainability, respectively. Median domain scores were 0.83, 0.61, 0.76 and 0.75. Median score of the entire tool was 0.67. HPPs operated by the Municipality scored lower than those of non-governmental organizations and health providers/organizations in every domain except for Project Management. CONCLUSION: A systematic city-wide evaluation of HPPs is feasible and uncovers strengths and weaknesses, including sustainability and variability by programme provider. Academic-community partnerships may assist planning and improving HPPs in the city.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".