The Prevalence of Technology-Facilitated Sexual Violence: A Meta-Analysis and Systematic Review
Bibliographic record
Abstract
The primary aim of this systematic review and meta-analysis was to examine the prevalence of technology-facilitated sexual violence (TFSV) within the adolescent and adult population regarding victimization and perpetration. In addition to the primary aim, associated health outcomes with TFSV were discussed through a qualitative lens. Specific forms of TFSV that were examined include distribution of, production of, and threats to distribute sexual material involving another individual without that person’s consent via images or videos; 425 articles from MEDLINE, PsycArticles, PsycINFO, Criminal Justice Abstracts, ProQuest Dissertations & Theses, and Google Scholar were screened. Nineteen articles (comprising 20 independent samples) reporting prevalence rates of TFSV on 32,247 participants were included in this random-effects meta-analysis. Pooled prevalence of victimization results revealed that 8.8% of people have had their image or video-based sexts shared without consent, 7.2% have been threatened with sext distribution, and 17.6% have had their image taken without permission. Regarding perpetration, 12% have shared sexts beyond the intended recipient, 2.7% have threatened to share sexts, and 8.9% have nonconsensually taken an image. Moderator variables included publication year, mean participant age, proportion of female participants, and study setting, with meta-regression analyses revealing no significant predictors. Finally, a qualitative analysis of nine articles ( n = 3,990) was conducted to assess mental health associations with TFSV victimization, revealing significant mental health impacts, including anxiety, depression, and poor coping, for victims.
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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 | 0.023 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".