Humanitarian Aid Workers Operating in Intentional Human-Made Catastrophe Contexts Mental Disorders State and Structure: A Systematic Review of quantitative research
Notice bibliographique
Résumé
Background: Humanitarian aid workers (HAWs) can operate in extreme contexts such as intentional human-made catastrophe contexts or IHMCCs (e.g., armed conflict, war) where, depending on their status, they can stay quite a long time. The ensuing psychological burden can be quite heavy. Our aim was to test the hypothesis that mental disorders were measured in isolation without considering any psychopathological structure, that is co- or multi-morbidity. Methods: We searched Embase, PsycNET, PubMed, and Web of Science databases for studies without date restriction. Quantitative research which took place in IHMCC that measured least one mental disorder (MD) as an outcome were included. Research that was not in English, did not have their own data collection, and that were not either cross-section, longitudinal, or sequential, were excluded. Biases were assessed with Newcastle-Ottawa Scale (NOS) and findings were combined in a narrative synthesis. Findings: Total number of included articles was eight nine. The initial total number of HAW was 1,859. Six out of eight studies were cross-sectional, one was a repeated-measure study, and one was longitudinal. Three studies did not report, or their numbers did not allow the calculation of any mental disorder prevalence. All but one study rated as poor on the Newcastle-Ottawa quality assessment scale. Based on the Grading of Recommendations, Assessment, Development and Evaluation, the overall quality of evidence for all outcomes was very low. Based on 2,150 participants and six studies, the pooled prevalence for anxiety was 24.46%, 95% CI [18.31%, 31.87%]; based on 1,943 participants and five studies, depression pooled prevalence was 30.84%, 95% CI [22.32%, 40.89%]; based on 1,996 participants and six studies, PTSD pooled prevalence was 9.09%, 95% CI [2.42%, 28.72%]. Difference between the pooled prevalence rates and diagnostic clinical interviews are statistically highly significant. The overall very low quality of evidence based of the selected studies, the substantial heterogeneity in the prevalence rates, and the small number of studies might limit the direct applicability of the findings. Mental health interventions for HAWs should focus on early identification and treatment. Screening should be applied with caution. Future research should focus on longitudinal studies and the development of standardized assessment tools.
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,170 | 0,060 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».