Health system evaluation in conflict-affected countries: a scoping review of approaches and methods
Notice bibliographique
Résumé
INTRODUCTION: Strengthening health systems in conflict-affected settings has become increasingly professionalised. However, evaluation remains challenging and often insufficiently documented in the literature. Many, particularly small-scale health system evaluations, are conducted by government bodies or non-governmental organisations (NGO) with limited capacity to publish their experiences. It is essential to identify the existing literature and main findings as a baseline for future efforts to evaluate the capacity and resilience of conflict-affected health systems. We thus aimed to synthesise the scope of methodological approaches and methods used in the peer-reviewed literature on health system evaluation in conflict-affected settings. METHODS: We conducted a scoping review using Arksey and O'Malley's method and synthesised findings using the WHO health system 'building blocks' framework. RESULTS: We included 58 eligible sources of 2,355 screened, which included examination of health systems or components in 26 conflict-affected countries, primarily South Sudan and Afghanistan (7 sources each), Democratic Republic of the Congo (6), and Palestine (5). Most sources (86%) were led by foreign academic institutes and international donors and focused on health services delivery (78%), with qualitative designs predominating (53%). Theoretical or conceptual grounding was extremely limited and study designs were not generally complex, as many sources (43%) were NGO project evaluations for international donors and relied on simple and lower-cost methods. Sources were also limited in terms of geography (e.g., limited coverage of the Americas region), by component (e.g., preferences for specific components such as service delivery), gendered (e.g., limited participation of women), and colonised (e.g., limited authorship and research leadership from affected countries). CONCLUSION: The evaluation literature in conflict-affected settings remains limited in scope and content, favouring simplified study designs and methods, and including those components and projects implemented or funded internationally. Many identified challenges and limitations (e.g., limited innovation/contextualisation, poor engagement with local actors, gender and language biases) could be mitigated with more rigorous and systematic evaluation approaches.
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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 | 0,009 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 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 | 0,000 |
| 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 ».