Assessments of health services availability in humanitarian emergencies: a review of assessments in Haiti and Sudan using a health systems approach
Bibliographic record
Abstract
BACKGROUND: Assessing the availability of health services during humanitarian emergencies is essential for understanding the capacities and weaknesses of disrupted health systems. To improve the consistency of health facilities assessments, the World Health Organization has proposed the use of the Health Resources Availability Mapping System (HeRAMS) developed in Darfur, Sudan as a standardized assessment tool for use in future acute and protracted crises. This study provides an evaluation of HeRAMS' comprehensiveness, and investigates the methods, quality and comprehensiveness of health facilities data and tools in Haiti, where HeRAMS was not used. METHODS AND FINDINGS: Tools and databases containing health facilities data in Haiti were collected using a snowball sampling technique, while HeRAMS was purposefully evaluated in Sudan. All collected tools were assessed for quality and comprehensiveness using a coding scheme based on the World Health Organization's health systems building blocks, the Global Health Cluster Suggested Set of Core Indicators and Benchmarks by Category, and the Sphere Humanitarian Charter and Minimum Standards in Humanitarian Response. Eight assessments and databases were located in Haiti, and covered a median of 3.5 of the 6 health system building blocks, 4.5 of the 14 Sphere standards, and 2 of the 9 Health Cluster indicators. None of the assessments covered all of the indicators in any of the assessment criteria and many lacked basic data, limiting the detail of analysis possible for calculating standardized benchmarks and indicators. In Sudan, HeRAMS collected data on 5 of the 6 health system building blocks, 13 of the 14 Sphere Standards, and collected data to allow the calculation of 7 of the 9 Health Cluster Core Indicators and Benchmarks. CONCLUSIONS: There is a need to agree upon essential health facilities data in disrupted health systems during humanitarian emergencies. Although the quality of the assessments in Haiti was generally poor, the large number of platforms and assessment tools deployed suggests that health facilities data can be collected even during acute emergencies. Further consensus is needed to establish essential criteria for data collection and to establish a core group of health systems assessment experts to be deployed during future emergencies.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".