What can we learn from the Jerusalem Community–Academic Partnership case study in an ultra-orthodox neighborhood?
Bibliographic record
Abstract
Community-academic partnerships can be useful models for sustainable interventions. The Jerusalem Community-Academic Partnership (J-CAP) was established to address local health needs identified by a population survey. It engaged stakeholders and public health students as part of their training. We describe the establishment and processes of this partnership over a 3-year period.Part 1 of the program entailed mapping and undertaking a quality assessment of health promotion (HP) programs in Jerusalem. Part 2 (Years 2 and 3), described herein, entailed a participatory process wherein a particular neighborhood, with a predominantly Ultra-Orthodox population, was chosen for intervention. A local steering committee was set up, and students assessed assets and needs by direct observation, in-depth interviews, and focus groups, followed by the development of intervention programs using a participatory process. Neighborhood assets and needs identified in the first year served as a basis for the participatory process of developing intervention programs. Assets identified included the local community center and swimming pool. Barriers to a healthy lifestyle included a lack of health literacy, time constraints, socioeconomic factors, and local lifestyle and environmental characteristics. Students focused on public spaces, preschool children, and young women and mothers when designing, together with local leaders, intervention programs related to healthy nutrition and physical activity. The participatory process contributed to strengthening partnerships among several services and agencies investing in the health of Jerusalem residents. The students' critical service-learning contributed to their understanding of HP in the real world and the local community. The students' reports, which were submitted to the community center management, could serve to inform future interventions.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".