Technology-Facilitated Sexual Violence: Prevalence, Risk, and Resiliency in Undergraduate Students
Bibliographic record
Abstract
Technology-Facilitated Sexual Violence (TFSV) is an understudied but prevalent phenomenon with initial research investigations demonstrating significant adverse consequences. TFSV is defined by unwanted sexual behaviors communicated and transmitted through digital means, which can include online/digital harassment, coercive sex-based communications, and sexuality-based harassment. The purpose of this study was threefold: (1) to examine prevalence rates of TFSV in males and females; (2) to assess the psychological symptoms associated with TFSV, and (3) to identify factors that could mitigate any negative psychological effects following TFSV victimization. Results indicated that overall prevalence rates of TFSV self-identifying victims in Canadian undergraduate students were 84.3%. Females were at increased risk of victimization, with prevalence rates as high as 87.9% and males reporting 74.3% as per the TFSV-V. Furthermore, self-identifying victims of TFSV tended to have lower levels of self-esteem and perceived control, and higher levels of depressive symptoms compared to those without TFSV experience. Regression analysis revealed that self-esteem, social support, and perceived control moderated the relationship between TFSV victimization and depressive symptoms. Limitations of the study and suggestions for further research investigating the impact of TFSV are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".