Descriptive Overview of Adolescent Health Indicators in Humanitarian Settings: A Cross-Country Analysis
Bibliographic record
Abstract
Purpose: An adolescent health information system is a relevantly new concept, especially in humanitarian settings. This article aims to map the available adolescent health indicators collected in selected humanitarian settings, identify the available data sources, and determine the alignment between these indicators and the draft list of priority indicators for adolescent health measurement recommended by the Global Action for the Measurement of Adolescent Health Advisory Group. Methods: We selected five countries experiencing humanitarian crises- Myanmar, Nigeria, Palestine, Ukraine, and Yemen. We identified the adolescent health indicators collected in each country using document analysis and a purposive sampling approach. We reviewed the primary population-based surveys used to gather adolescent health data and noted the most recent year each survey was conducted. The identified indicators were then categorized by measurement domains and specific areas of adolescent health.Results: The Multiple Indicator Cluster Survey and Demographic Health Survey were conducted in all five countries selected, but three out of five countries have not administered either within the last five years. Yemen and Palestine only included married women in their sample sizes, and no one younger than 15 was interviewed. Indicators most commonly assess reproductive health, tobacco use, and adolescent fertility. Limited data was found on younger adolescents, males, water, sanitation, hygiene, disability, and nutrition indicators. Discussion: Adolescent health information in humanitarian crises requires more frequent surveys, including all adolescent age groups, and unique data collection methodologies. The current surveys used to measure adolescent health indicators have limited ability to be inclusive to all adolescents. It is important to establish a list of priority indicators deemed essential in humanitarian settings and relevant ways to collect them.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".