A Qualitative Exploratory Case Study of the Safe Doors, Safe Homes Intervention for Intimate Partner Violence Prevention in the Canadian Context: Perspectives from IPV Survivors, Service Providers and Police
Bibliographic record
Abstract
Purpose: Intimate partner violence (IPV) against women is a pervasive public health issue, leading to heightened rates of morbidity and mortality. This study aimed to assess the feasibility and acceptability of implementing the Safe Doors, Safe Homes (SDSH) structural safety intervention in Canada, which seeks to enhance the safety of female IPV survivors within their homes. Methods: = 14). Semi-structured interviews were conducted to explore the SDSH intervention's feasibility and acceptability. Results: Content analyses of interviews identified barriers and facilitators for implementation, including (but not limited to): safety risks, challenges with temporary housing, intervention discretion, maintenance, the intervention's ability to reduce safety risks, its potential flexibility, and the availability of funding. Process considerations for implementing the SDSH intervention in Canada were described. Overall, most participants deemed the intervention necessary, with a small number of police officers also describing home relocation as a reasonable strategy for preventing revictimization. Conclusions: This study provides insights into potential barriers and facilitators for implementing the SDSH intervention in the Canadian context. This knowledge is pivotal for informing adaptations that align with survivors' unique needs, ensuring the success and safety of ongoing IPV prevention efforts. These findings establish a foundation for advancing the SDSH intervention conceptually and practically, offering valuable considerations for refining IPV interventions on practical and systemic levels.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".