Prevalence of mental disorders among children exposed to war: a systematic review of 7,920 children
Bibliographic record
Abstract
Worldwide, millions of children are affected by armed conflict. However, data on the prevalence of mental disorders among these children is sparse. We aimed to determine the prevalence of mental disorders among children affected by war using a systematic review and meta-regression analysis. We systematically reviewed existing literature to identify studies on prevalence of post-traumatic stress disorder (PTSD), anxiety, depression and psychosis among children exposed to armed conflict. We searched electronic databases and references listed in studies to obtain eligible studies. We pooled studies using the random-effects method and explored heterogeneity using meta-regression analysis. Seventeen studies met our inclusion criteria. Studies included 7,920 children. Sample sizes ranged from 22 to 2,976. Four studies were conducted during a conflict and others during post-conflict. All the studies reported PTSD as the primary outcome ranging from 4.5 to 89.3%, with an overall pooled estimate of 47% (9% CI: 35-60%, I2 = 98%). Meta-analysis heterogeneity was attributable to study location (OR 1.33, 95% CI: 1.27-1.41), method of measurement (OR 1.36, 95% CI: 1.29-1.44) and duration since exposure to war (coefficient 0.17, 95% CI: 0.94-0.25). In addition, four studies reported elevated depression that allowed pooling (43%, 95% CI: 31-55%) and three studies reported elevated anxiety disorders allowing pooling (27%, 95% CI: 21-33%). Our systematic review suggests a higher prevalence rate of mental disorders among children exposed to conflict than among the general population. Given the number of current conflicts, there is a paucity of information regarding mental disorders among children affected by war.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".