Designing an integrated program for intimate partner violence screening and referral at a hospital trauma service in Alberta, Canada
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) is the primary cause of serious injury and the second leading cause of death among women of reproductive age in Canada. Alberta has one of the highest rates of IPV in Canada. Health systems play a crucial role in providing services for IPV. It is widely recommended to integrate IPV services into trauma services to improve outcomes for patients admitted with severe IPV injuries. For example, having a dedicated IPV expert, such as a peer advocate worker or social worker on the trauma team, will help patients experiencing IPV get the support they need to avoid further harm. Approach: The University of Alberta Hospital (UAH) has a large trauma population at risk for IPV and is currently without a standardized screening protocol, resources to appropriately screen, and trained IPV personnel to provide resources for safety planning, mental health support, and referral to community-based IPV services. Our team has collected evidence on effective IPV screening practices and identified the determinants of successfully implementing integrated IPV response services in trauma care. We gathered qualitative feedback from trauma providers and IPV survivors to design a comprehensive screening program that includes a dedicated IPV expert on the trauma team, referral to community-based resources, and educational sessions for hospital staff delivered by community IPV collaborators. Results: Consistent with a social-ecological analysis and implementation science, we identified the factors that would enable the successful implementation of integrated IPV services at the UAH trauma service. In the wider community context, we have engaged with community agencies and Indigenous knowledge experts, including the UAH Indigenous Liaison, to develop a referral protocol to community-based IPV resources that is sensitive to survivors needs, particularly within specific cultural groups such as Indigenous patients. Within the UAH trauma service itself, our site champion, a trauma surgeon, has engaged UAH leadership to support the initiative. We propose hiring a peer advocate worker to administer IPV screening using a validated screening tool. Having dedicated personnel to support screening, assessment, and referral addresses trauma providers time constraints and capacity to respond to IPV. At the micro level, the peer-advocate worker will establish rapport with patients, assess risk and protective factors, educate patients about IPV, and provide direct connections to community resources and follow-up. Implications: We investigated the factors that impact the successful implementation of an integrated IPV program at the UAH trauma service. These factors guided the development of an IPV screening and referral program at the UAH trauma service, and we have submitted a funding proposal to support its implementation. Our research advances knowledge aligned with the nine pillars of integrated care, namely, supporting an integrated workforce for IPV response in the health system and promoting survivor-centred care by improving trauma patients connection to IPV resources and community supports. Peer-advocate workers also support care coordination around patient needs and preferences. For the healthcare system, breaking the cycle of violence can reduce trauma recidivism and repeat presentations to hospitals for acute injuries and potentially prevent death from ongoing IPV.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".