Technology-facilitated sexual violence and suicide risk: A serial mediation model investigating bullying, depression, perceived burdensomeness, and thwarted belongingness
Bibliographic record
Abstract
Technology-facilitated sexual violence (TFSV) is a comprehensive term used to encompass all sexually aggressive and harassing behaviours involving technology. Although there is a growing knowledge base investigating the prevalence and consequences of TFSV, relatively little is known about the extent of aversive consequences experienced by victims and the pathways from victimization to suicidal affect, cognition, and behaviour. TFSV victimization and subsequent suicide has been a subject of several high-profile media cases in recent years. We examine TFSV in relation to two main constructs embedded within the interpersonal theory of suicide (ITS), perceived burdensomeness (PB) and thwarted belongingness (TB). Quantitative survey data ( N = 521) were used to evaluate PB and TB in the context of TFSV victimization. The objective was to analyze mechanisms underlying the relationship between TFSV victimization and suicide risk, exclusively accounting for mediating factors of interpersonal victimization, depression, TB, and PB. Pathway results showed that TFSV victimization increased suicide risk (i.e., suicidal affect, cognition, and behaviour) serially through bullying, depression, and PB—suggesting a cascade of victimization experiences. TB was not a significant mediator. The present results provide novel quantitative data substantiating the devastating risks of TFSV victimization and thus evidencing the importance of legal protections for victims of TFSV.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".