Domestic Abuse Screening: Normalizing Assessment at Triage through Simulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Research suggests those residing in rural and remote locations across the province are more vulnerable to domestic violence (DV), with rates of DV and domestic homicide being three times higher than in urban areas (Canadian Domestic Homicide Prevention Initiative, 2019). Alberta has the third highest rate of police reported DV cases across Canada (Statistics Canada, 2019). Complex social determinants leading to this increase include geographic and social isolation, economic barriers, traditional social values, barriers to services, public visibility and the prevalence and normalization of firearms (Canadian Domestic Homicide Prevention Initiative, 2019). This simulation helps participants to recognize signs of possible domestic violence using screening tool at triage, understand and identify local processes available for domestic abuse and what resources their care area may have for victims. Methods: To support and empower frontline healthcare professionals to recognize and respond to domestic violence within the healthcare setting, a partnership between South Zone (SZ) educators, the AHS (Alberta Health Services) eSIM team and the Provincial Domestic Abuse Response Team (DART) was created. Through a shared vision of empowering frontline healthcare staff, a unique Domestic Violence Screening Scenario was developed. Utilizing a flipped classroom approach, participants are provided didactic education prior to participating in the SBE, followed by a debrief using an adapted PEARLS framework approach. The approach provides a psychologically safe, judgement free environment to engage in critical reflexivity and discussion of the complex social determinants that place those residing in rural and remote areas at additional risk of experiencing DV (Canadian Domestic Homicide Prevention Initiative, 2019). Additionally, participants are given the opportunity to practice utilization of the DV universal screening tool and develop understanding of the importance of routine screening within a healthcare setting. Currently this education scenario is provided during the annual Rural Skills Days at each of our south zone sites. Evaluation Methods: The SZ educators, AHS eSIMs Provincial Scientific team and DART developed several evaluation tools to capture data regarding the impact and applicability of the SBE Domestic Violence Screening Scenario, both on participants and patients seeking support for domestic violence in the emergency department setting. Data collection activities to measure the outcomes and impact of the SBE include: A pre- and post-SBE quantitative survey, which includes effective self-reported measures on: (1) attitudes of personal bias (2) communication strategies and (3) awareness of organizational resources. Participants will be contacted 3 and 6 months after the session for the following: Completion of the same evaluation survey (pre- and post-SBE) to capture data regarding the applicability of SBE learning to their clinical role in the emergency department. Participation in semi-structured interviews to collect rich data concerning applicability of the SBE, longitudinal impact on clinical practice and participants success in integrating domestic violence screening. Results: As a result of the SBE pre-qualitative evaluation surveys and post simulation debriefing sessions, participants are more likely to admit discomfort in caring for patients who are experiencing DV, due to lack of knowledge and training in this area. Through the pre-session evaluation survey and the post-session debrief period, many participants shared discomfort in caring for patients experiencing DV. Furthermore, due to a lack of knowledge and training in this area of practice, many professionals disclosed avoiding asking patients about DV even when injuries point to violence in the home. Advice and Lessons Learned: We have found Rural Emergency Department nurses are not comfortable with asking the domestic violence screening question due to discomfort with the subject matter, dual relationships with patients living in the same small communities, and lack of experience when building rapport with patients who are experiencing DV. We recommend the following considerations when implimenting a similar educational opportunity to support healthcare professionals to practice DV screening and to build skills needed to support patients experiencing DV: Utilization of a scaffold approach. Collaboration with content experts such as DART team members is imperative when running simulations to standardize how DV patients presenting to the ED (Emergency Department) are cared for provincially. It is important to use an inter-professional approach to strengthen the ED team to assist in role clarity when caring for patients experiencing DV.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it