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Record W4408961665 · doi:10.1080/13552600.2025.2482916

Victim-blaming in sexual violence against sex workers: a systematic review

2025· review· en· W4408961665 on OpenAlexaboutno aff
Judith Velasco, Francisco J. Sanmartín

Bibliographic record

VenueJournal of Sexual Aggression · 2025
Typereview
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsPoison controlOccupational safety and healthHuman factors and ergonomicsSuicide preventionInjury preventionPsychologySexual violenceSex offenseCriminologyMedical emergencyMedicineSexual abuse

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.384
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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