Novel simulation-based education used for Domestic Abuse Screening for Emergency Department Healthcare Professionals
Bibliographic record
Abstract
Purpose: Rural emergency department healthcare professionals are well-positioned to serve as a resource to domestic violence victims but may lack the necessary training to effectively screen for and respond to domestic violence victims. A novel, low fidelity simulation-based education provided an opportunity to increase healthcare professionals’ readiness to screen for domestic violence. Methods: We facilitated 14 simulations with 181 participants at eight rural sites between September 2022 and June 2023. A multi-method program evaluation was used to assess the impact of the simulation-based education on participants' readiness to screen for domestic violence and their clinical practice. Participants of the simulation were invited to complete a pre and post-simulation survey regarding their experience and perceptions related to screening for domestic violence. Participants were invited to participate in follow-up semi-structured interviews to discuss how simulation-based education has influenced their practice three to nine months following the simulation. Results: Prior to the simulation-based education participants reported several barriers to screening for domestic violence and a lack of training. Following simulation-based education, a statistically significant increase in readiness to screen for domestic violence was observed, t (102) = 19.43, p <.001, d = 1.91 between the pre- and post-simulation survey scores (n = 103). Through semi-structured interviews (n = 6), two themes were identified: (a) influence on education and (b) the power of simulation-based education. Conclusions: Simulation-based education is an effective education modality to enhance healthcare professionals’ readiness to screen patients for domestic violence and may positively influence their practice through increased awareness and more consistent screening.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".