Prevalence of sexual violence among refugees: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To synthesize data about the prevalence of sexual violence (SV) among refugees around the world. METHODS: A systematic review was conducted from the search in seven bibliographic databases. Studies on the prevalence of SV among refugees and asylum seekers of any country, sex or age, whether in English, French, Spanish and Portuguese, were eligible. RESULTS: Of the 2,906 titles found, 60 articles were selected. The reported prevalence of SV was largely variable (0% to 99.8%). Reports of SV were collected in all continents, with 42% of the articles mentioning it in refugees from Africa (prevalence from 1.3% to 100%). The rape was the most reported SV in 65% of the studies (prevalence from 0% to 90.9%). The main victims were women in 89% of the studies, all the way, especially when still in the countries of origin. The SV was perpetrated particularly by intimate partners, but also by agents of supposed protection. Few studies have reported SV in men and children; the prevalence reached up to 39.3% and 90.9%, respectively. Approximately one-third of the studies (32%) were carried out in refugee camps and more than half (52%) in health services using mental health assessment tools. No study has addressed the most recent migratory crisis. Meta-analysis was not performed due to the methodological heterogeneity of the studies. CONCLUSIONS: SV is a prevalent problem affecting refugees of both sexes, of all ages, throughout the migratory journey, particularly those from Africa. Protection measures are urgently needed, and further studies, with more appropriate tools, may better measure the current magnitude of the problem.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".