Gender differences in post-traumatic symptomatology among refugees: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Armed conflicts and persecution force millions of people to flee their countries, exposing them to severe pre-, peri-, and post-migration stressors that can negatively affect mental health. Post-traumatic stress disorder (PTSD), anxiety, and depression are common among forcibly displaced populations, and evidence suggests that gender may influence these outcomes.Objective: This systematic review and meta-analysis examined gender differences in mental health among refugees displaced by armed conflict, focusing on: (1) the odds and frequency of individuals presenting post-traumatic symptoms, (2) common comorbid conditions such as anxiety and depression, and (3) gender-based differences in exposure to traumatic events.Methods: Following PRISMA guidelines, we searched PubMed, Web of Science, Scopus, and Science Direct (December 2024). Two independent reviewers screened and extracted data according to PICOS criteria, and study quality was evaluated using the Newcastle–Ottawa Scale. Twenty-five studies met inclusion criteria, and 14 provided quantitative data for meta-analysis. Random-effects models were computed in R to estimate gender-based effect sizes for PTSD and related outcomes.Results: Women showed higher odds and frequency of PTSD symptoms, as well as increased levels of anxiety and depression, although differences in PTSD symptom severity were not consistently significant. Gender differences were also observed in exposure to traumatic events: women reported higher rates of sexual violence, whereas men were more likely to have been kidnapped or imprisoned. Longitudinal findings indicated a chronic trajectory of PTSD symptoms for both genders, likely shaped by post-migration stressors.Discussion: These findings highlight the relevance of gender in post-traumatic mental health among refugees. Higher symptom levels in women may reflect disproportionate exposure to gender-based violence and unequal access to support resources. Addressing these disparities is essential for designing gender-sensitive interventions and policies. Further research is needed to clarify long-term trajectories, standardize assessment tools, and identify effective strategies for diverse refugee populations.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".