Assessments of health services availability in humanitarian emergencies: a review of assessments in Haiti and Sudan using a health systems approach
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,006 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».