Mapping stakeholders, services, data, and the information system for adolescent health in the West Bank
Bibliographic record
Abstract
BACKGROUND: Adolescent health plays a crucial role in shaping lifelong well-being, yet significant gaps exist in addressing adolescent health needs. In conflict-affected regions like the West Bank, fragmented service delivery, inconsistent data collection, and lack of coordination between providers undermine the effectiveness of health services. An efficient health information system ensures accurate data collection, stakeholder integration, and evidence-based decision-making. This study aims to map the landscape of available adolescent health services in the West Bank, clarify the key service providers, determine the existing data sources, and describe the health information system supporting adolescent health. METHODS: This study utilized a comprehensive landscape analysis to assess adolescent health services in the West Bank. Stakeholder mapping and interviews identified key stakeholders and assessed their roles in service delivery and the health information system. Ethical approval was obtained, and all participants provided informed consent. Data were collected from different healthcare organizations, including governmental bodies, non-governmental organizations, and private facilities. Thematic analysis was performed on interview data, and geospatial analysis was used to visualize the distribution of services and providers across 11 governorates using ArcMap 10.5. RESULTS: Governmental bodies, non-governmental organizations, and private entities were the predominant providers of adolescent health services in the West Bank. These services were primarily delivered through healthcare facilities, educational institutions, youth centers, and select population-based programs, including vaccination initiatives and the 121 hotline, which provides free psychological support to victims of violence. The adolescent health information system in the West Bank was fragmented, with inconsistent data collection across providers. Governmental, NGO, and private sector organizations use different data systems. Each type of provider used population-based surveys as the primary source of health data. However, there were limitations in the availability of routine data. CONCLUSIONS: This study represents the first comprehensive mapping of adolescent stakeholders and services in the West Bank. Identifying the existing services accessible to adolescents and their providers establishes a foundation for developing target policies and programs that address the current gap and needs of adolescents in the West Bank.
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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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".