Lifetime sexual violence experienced by women asylum seekers and refugees hosted in high-income countries: Literature review and meta-analysis
Bibliographic record
Abstract
Screening and care for victims of sexual violence (SV) among asylum seekers and refugees (ASRs) living in High-income host countries were prioritized by the WHO in 2020. The lack of stabilized prevalence findings on lifetime SV among ASRs in High-income countries hinders the development of adequate health management. The objective of this study was to determine the lifetime prevalence of SV experienced by ASRs living in High-income countries. We conducted a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were included in the meta-analysis if the sample consisted exclusively of asylum seekers or refugees over the age of 16 living in High-income countries and if they reported a lifetime prevalence of experienced SV. The results of the meta-analysis were expressed with 95 % confidence intervals (CIs) as estimates of lifetime SV prevalence using a random-effects model. The estimated lifetime prevalence of SV among women ASRs was 44 % (95 % CI, 0.24-0.67) and 27 % (95 % CI, 0.18-0.38) for both sexes. This meta-analysis revealed a high prevalence of SV among ASRs hosted in High-income countries and suggest the importance of developing specific screening and care programs in these host countries.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".