Victim-blaming in sexual violence against sex workers: a systematic review
Bibliographic record
Abstract
Sex workers face higher risks of sexual violence and victim-blaming than the general population, yet this intersection remains underexplored. This systematic review synthesizes existing literature on victim-blaming of sex workers and examines contributing factors. A systematic review (CRD42024579705) was conducted using Web of Science, PubMed, Scopus, and PsycInfo to identify peer-reviewed articles in English and Spanish, with no time restrictions. Study quality was assessed using the Newcastle-Ottawa Scale. Of 80 studies identified, 10 met inclusion criteria. Sex workers were more often blamed for their victimization than general population, seen as less credible, and viewed as deserving of violence. Victim-blaming was linked to poorer mental health and lower rates of reporting or help-seeking. Factors contributing to higher victim-blaming included being male, no trauma history, support for sex work criminalization, and endorsing sex work-related myths. Findings underscore the need for interventions targeting myths and stereotypes through societal and professional education.PRACTICE IMPACT STATEMENT Victim-blaming against sex workers experiencing sexual violence exacerbates trauma and deters reporting. Addressing societal myths and biases, these findings advocate for stigma reduction programs and informed policies, especially in law enforcement and healthcare, to enhance justice access and support for sex workers.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".