Implementing a trauma‐informed intervention for homeless female survivors of gender‐based violence: Lessons learned in a large Canadian urban centre
Bibliographic record
Abstract
Gender-based violence is associated with an elevated risk of physical and psychological harm for girls and women. This study examines service user and provider experiences of a trauma-informed, peer-facilitated group psychosocial intervention (Peer Education and Connection through Empowerment [PEACE]) targeting female-identified youth experiencing homelessness and gender-based violence. Participants were recruited among service users and providers of the intervention, delivered in Toronto, Canada. We conducted 19 semi-structured interviews between May and October 2017, engaging 12 service users and 7 additional stakeholders (including social service providers, peers and program administrators). We elicited participant perspectives on the acceptability of the intervention and key enablers of successful implementation and engagement of the target population. Qualitative transcripts were analysed using thematic analysis. Service users, including survivors of sexual exploitation, forced marriage and honour crimes, described satisfaction with and acceptability of the intervention. A number of factors were perceived by service users and providers as contributing to the intervention's successful implementation, including a focus on service user needs, program quality, flexibility and accessibility and strong inter-and intra-agency networks. Introducing peers as mentors led to challenges that could be mitigated through peer mentor education and training to maintain healthy boundaries and enhance peer mentor retention. The need for clear guidelines on the management of trauma disclosures in community settings and a systems-wide approach to service provider and administrator training in the effective integration of peer support services also emerged as important areas for future development. A group-based, trauma-informed and peer-supported psychosocial intervention was acceptable to service users and providers and successfully engaged female-identified survivors of gender-based violence who were also experiencing homelessness. Findings add to the scant knowledge base on interventions to support this population and identify important areas for future research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".