Associations between density and quality of health promotion programmes and built environment features across Jerusalem
Bibliographic record
Abstract
BACKGROUND: Health promotion programmes (HPPs) have the potential to influence individual health, depending on their quality and characteristics. Little is known about how they interact with built environment features and neighbourhood demographics in cities with substantial health disparities. METHODS: Using the European Quality Instrument for Health Promotion (EQUIHP), we assessed the quality of HPPs, operating between 2016 and 2017, among adults aged 18-75 in Jerusalem. Areas were characterized by ethnicity and area socioeconomic level. Health information (body mass index, physical activity level) was obtained from the city profile survey. Geospatial information on the location and length of walking paths and bicycle lanes was obtained. Spearman correlations were used to assess associations among variables. RESULTS: Ninety-three HPPs operating in 349 locations in Jerusalem were identified. Programmes were unevenly distributed across urban planning areas (UPAs), with the highest density observed in the southwest, areas populated mainly by non-orthodox Jewish residents. However, the best performing HPPs based on EQUIHP score were in the north and east UPAs, inhabited primarily by Arab residents. At a neighbourhood level, characteristics of the built environment positively correlated with higher total EQUIHP scores: the ratio between walking lane length to the neighbourhood's population size (r = 0.413, P < 0.001) and length of bicycle lane per population (r = 0.309, P = 0.5). Median EQUIHP score negatively correlated with the number of programmes per neighbourhood size (m2) (r = -0.327, P = 0.006) and neighbourhood average socioeconomic status (SES; r = -0.266, P = 0.027). CONCLUSIONS: Our findings suggest that higher quality HPPs were preferentially located in areas of lower SES and served minority populations in Jerusalem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".