Humanitarian Aid Workers Operating in Intentional Human-Made Catastrophe Contexts Mental Disorders State and Structure: A Systematic Review of quantitative research
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.170 | 0.060 |
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; both teacher heads agree on what is shown here.
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".