A Collaborative Response to Addressing Family Violence with Racialized and Diverse Communities During Pandemic Recovery in Peel Region
Bibliographic record
Abstract
Family violence is a social issue that impacts families and communities every day in Canada and around the world. As family violence rates continue to increase there is an urgent need for cross-sectoral collaboration to codesign social work and social service systems, in partnership with those experiencing family violence. This article will share learnings from a two-year community-based participatory research study that worked alongside survivors and witnesses of family violence, community partners from diverse social service agencies, and researchers, to understand experiences of family violence in racialized communities in Peel region, Ontario, Canada. An intersectional-trauma-informed approach guided the work that included establishing a community advisory board, hiring peer research assistants, Photovoice, and holding a knowledge exchange event (KEE) with survivors and witnesses of family violence, researchers, and community partners to rapidly generate ideas for intervention areas through a 25/10 crowdsourcing activity and codesign preliminary solutions through a mini hackathon. Key findings from the photovoice highlighted systemic failures and gaps experienced by those facing family violence. As we shifted into ideation, this preliminary focus on systems solidified and top ideas identified included barrier-free, culturally aware provision of services ranging from mental health supports, safe housing, financial independence, and accessing wrap-around services. Our work concluded with the collaborative development of preliminary solutions to these ideas and emphasized the need for cross-sectoral partnerships and lived experience engagement to change systems. Centering the voices of those who have experienced FV in system-level change and advocacy is necessary to ensure services and supports meet the needs of service users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.031 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.011 |
| 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".