From Awareness to Action: Shaping a Unified Response to Intimate Partner Violence (IPV) and Femicide in Windsor-Essex
Bibliographic record
Abstract
Despite growing awareness, Intimate Partner Violence (IPV) remains a pressing public health crisis in Canada. Declared an epidemic in Windsor-Essex and over 90 municipalities across Ontario, IPV leads to devastating social and health consequences, including femicide. Over the past three years, in Ontario alone, there has been an average of one or more femicides per week (OAITH, 2024). Research confirms that cross-sector collaboration is essential to addressing the multi-dimensional health impacts of IPV, as coordinated responses across healthcare, social services, law enforcement, and community organizations improve outcomes for survivors (Brown et al., 2023; Douglas & Hines, 2011). However, fragmented training, siloed approaches, inconsistent use of risk identification tools, and sector-specific service differences limit these efforts (Aarons et al., 2014). Consultations with the Windsor-Essex IPV/GBV Leadership Table, comprised of diverse sector representatives identified a critical need for shared language, standardized risk assessment tools, and cross-sector training to enhance coordination. To avoid reinventing the wheel, this project, aligns with the Violence Against Women Coordinating Committee's approach by adapting an existing public health framework originally developed for coordinated suicide prevention, and integrates publicly available IPV awareness materials to create a scalable, evidence-based training module. This presentation will share stakeholder insights and the module's development to date, demonstrating how public health strategies enhance IPV training and prevention efforts. By co-developing a standardized, cross-sector training framework, this initiative strengthens multi-agency collaboration, ensuring every frontline service provider is equipped to respond effectively.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".