“I’m Not Really Sure of What Exists Here”: Frontline Perspectives on Services for Technology-Facilitated Sexual Violence
Bibliographic record
Abstract
Technology-facilitated sexual violence (TFSV) is a pervasive issue, yet research on the availability, accessibility, and effectiveness of services for TFSV victim-survivors remains limited, particularly in Canada. This qualitative study examines the state of service provision for TFSV in Nova Scotia, drawing on insights from 12 interviews with service providers specializing in sexual violence prevention and response. The study explores the availability and nature of services offered to TFSV victim-survivors and the key challenges faced by professionals in providing support. Thematic analysis highlights significant gaps in service provision, including a reliance on generalized approaches, restrictive eligibility criteria that exclude certain victim-survivors, and a widespread lack of awareness regarding available services. Service providers also reported major challenges to integrating TFSV into existing services, such as the absence of specialized training, inadequate strategies for addressing the technological dimensions of TFSV, and ongoing tensions between generalist and specialized approaches to service delivery. While many existing sexual violence services remain accessible to TFSV victim-survivors, current systems are not fully equipped to address the complexities of technology-facilitated violence. Findings underscore the urgent need for tailored training, expanded eligibility criteria, and specialized resources to improve service provision for TFSV victim-survivors in Nova Scotia and beyond.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".