Improving Formal Support After Sexual Assault: Recommendations From Survivors Living in Poverty in Canada
Bibliographic record
Abstract
Despite the extensive and often long-lasting sequelae of sexual assault, a significant majority of survivors do not seek assistance from formal support sources, such as police, medical or mental health professionals, and community-based agencies. While growing attention has been directed toward understanding the help-seeking barriers faced by sexual assault survivors, few studies have explored the experiences of survivors living in poverty and how help-seeking can be improved for this marginalized population. The present study sought to understand these issues from perspectives of survivors themselves. Women sexual assault survivors who self-identified as living in poverty ( n = 15) were recruited from community-based agencies in the Greater Toronto Area, and participated in semistructured interviews focused on their personal decisions around help-seeking and broader views on access to and quality of formal assistance. Two research questions were of interest. First, what can be done to encourage service use among survivors living in poverty? Second, what is needed to improve the quality of assistance they receive? Thematic analysis revealed three key themes around which participants’ recommendations centered: (a) increased accessibility of information, (b) improved training for formal support providers, and (c) greater opportunities for empowerment. Practical implications of these findings improving the service utilization rates and experiences of formal help-seeking among marginalized women survivors of sexual assault 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".