The Frequency, Nature, Impact, and Coping Strategies of Nonconsensual Intimate Image Dissemination Victimization: A Scoping Review
Bibliographic record
Abstract
Young adults increasingly initiate, maintain, and end sexual relationships online, an evolution that has also transformed how sexual violence may be perpetrated. Nonconsensual intimate image dissemination (NCIID) has gained attention in research, policy, and media. Yet, to date, there has been no synthesis of the literature on NCIID victimization. The goals of this review were to: (a) describe the frequency and nature of NCIID victimization, (b) examine the impacts of experiencing NCIID, and (c) identify survivor coping strategies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, PubMed, Scopus, Web of Science, and ProQuest were systematically searched for peer-reviewed qualitative, quantitative, and mixed-methods studies published in English by February 1, 2025. A total of 49 studies met the inclusion criteria. The reported frequency of NCIID ranged from 3% to 65%, with higher rates among those who experienced some other form of technology-facilitated sexual violence. Perpetrators were often current or former partners, and content was shared through both private messaging and public platforms. Victim-survivors frequently reported psychological (e.g., depression, anxiety, post-traumatic stress disorder), social (e.g., ostracism, victim-blaming), and academic/occupational harms. Help-seeking strategies included disclosing to trusted others, legal action, and content reporting, while coping through avoidance strategies included relocation, withdrawal, or trying to act as if nothing happened. Barriers to help-seeking included stigma, lack of awareness, and prior negative experiences with authorities. Findings highlight the urgent need for survivor-centered support systems, awareness campaigns, and broader conversations about consent in digitally mediated sexual encounters.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".