Improving Experimental Designs for Interventions to Reduce Intimate Partner Violence: Protocol for Refinements to Single-Case Experimental Design for a Safety Planning Intervention in Ontario, Canada
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) affects 2 in 5 women in Canada, leading to both physical and mental health consequences. Safety planning is a secondary prevention intervention designed to assist those experiencing IPV in taking steps to increase their safety and decrease contact with their abusive partner. Safety planning has been shown to help survivors mitigate the negative mental health effects of IPV and encourage actions to increase safety, but evaluation outside the United States remains limited. Objective: Our team plans to evaluate the use of single-case experimental design (SCED) to assess the effectiveness of a web-based safety planning app (WITHWomen Pathways) for women experiencing IPV in the Greater Toronto Area. The study also explores whether women can be safely engaged for intense follow-up. Methods: SCED evaluation will involve multiple baseline and postintervention assessments of a small number of women experiencing IPV (n=6). Participants will be recruited from IPV service organizations across the Greater Toronto Area. SCED will allow for rigorous within-subject comparison, using repeated measurements at multiple time points for 3 primary outcomes (decisional conflict, empowerment to take safety actions, and use of safety strategies) and 2 secondary outcomes (mental health and IPV recurrence). The evaluation will include 5 phases: recruitment, eligibility screening, multiple baseline interviews, intervention (web app delivery), and multiple postintervention assessments. In this paper, we also discuss the development of rigorous protocols for maintaining safety and flexible data collection methods (in person, via telephone, or online). Results: Recruitment began on July 3, 2024. As of December 2025, a total of 4 participants have been recruited and have completed multiple baseline assessments. Data analysis has been completed for 4 participants, and results are expected to be published in spring 2026. Conclusions: The SCED approach may offer a novel and ethical evaluation method for IPV interventions. If effective, the WITHWomen Pathways web app could significantly improve help-seeking and safety planning among women experiencing IPV in the GTA. This study may serve as a model for future IPV intervention evaluations, demonstrating robust evaluation data and participant safety.
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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 | 0.065 | 0.065 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.098 | 0.009 |
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".