Opportunities and challenges of using a health information system in adolescent health management: A qualitative study of healthcare providers’ perspectives in the West Bank, occupied Palestinian territory
Bibliographic record
Abstract
BACKGROUND: Adolescents are a critical demographic facing unique health challenges who are further impacted in humanitarian settings. This article focuses on the urgent need for a structured health information system (HIS) to address the gaps in data availability and evidence-based interventions for adolescent health. The study aims to identify opportunities and challenges in utilizing the HIS to enhance adolescent health in the West Bank by gathering insights from healthcare providers. METHODS: Semi-structured key informant interviews were conducted with participants involved in the HIS regarding adolescent health in the West Bank. They were selected by purposive sampling. Nineteen interviews were conducted between July and October 2022, and thematic analysis was carried out using MAXQDA software. RESULTS: The opportunities identified were the small-scale victories the participants described in building the HIS for adolescent health. These included institutional and individual capacity building, digitalizing parts of the HIS, connection fragmentation of adolescent health activities, multi-sectoral collaboration, reorienting services based on health information, working with limited resources, enhancing community engagement to encourage ownership and active participation, and taking strategic actions for adolescents for information. The challenges were the high workload of staff, lack of health information specialists, limited resources, lack of a unified system in data collection, lack of data on essential indicators, data quality, data sharing, and data sources and use. CONCLUSION: This study showed the potential of the HIS with capacity building, digitization, and collaborative initiatives; it also suffers from issues like staff shortages, non-standardized data collection, and insufficient data for essential indicators. To maximize the impact of the HIS, urgent attention to staff shortages through comprehensive training programs, standardization of data collection systems, and development of a unified core indicator list for adolescent health is recommended. Embracing these measures will allow the HIS to provide evidence-based adolescent health programs, even in resource-constrained and complex humanitarian settings.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".