Combined programming to support women-identified survivors of domestic violence experiencing co-occurring problematic substance use
Bibliographic record
Abstract
Women-identified survivors of domestic violence (DV) experiencing co-occurring problematic substance use (SU) have complex service needs which are inadequately addressed by traditional siloed approaches to service delivery. Combined programming, which simultaneously addresses needs related to both DV and SU, demonstrates effectiveness in addressing the needs of this population. However, research is only recently emerging, and agencies have been slow to incorporate combined interventions. Addressing this critical gap, this qualitative study explored the strengths and limitations of community-based agencies in Canada offering combined DV/SU programming. Using an Interpretive Description design, semi-structured interviews with service providers offering combined DV/SU programming were used to explore the specific motivations, theoretical approaches, policies, successes, and barriers to implementing such programming. The study found that successful programming was informed by trauma-informed, client-centered, and harm reduction approaches, and focused on addressing DV/SU needs holistically. The study also uniquely described the unanticipated impacts of the COVID-19 pandemic, including increases in complex client needs and limitations placed on community-based agencies, in motivating practice shifts towards combined interventions. Practice recommendations garnered through participant interviews included efforts to increase staff competencies, address stigma and misconceptions, build agency capacity, and further incorporate trauma-informed, client-centered, and harm reduction approaches. This study provides useful insights for future research, policies, and supports that would address the unique needs of women experiencing DV/SU.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".