Using Simulation to Train Service Providers in Responding to Intimate Partner Violence (IPV) and Trauma
Bibliographic record
Abstract
This study used simulation-based research (SBR) to gain a better understanding of how intimate partner violence (IPV) service providers engage in trauma-informed practice in a simulated session with a standardized patient. Our qualitative study recruited 18 IPV service providers from Canada and the United States. Each participant engaged in a virtual 30-minute case-based simulated session with an actor portraying a survivor of IPV experiencing vulnerabilities related to violence and immigration status. Following each simulation, IPV service providers participated in a 30- to 45-minute reflective dialogue. The data were analyzed using reflexive thematic analysis. Identified themes included: (a) simulation builds trauma-informed responses that recognize intersecting identities (e.g., race, immigration status, culture) and (b) simulation helps train service providers in responding to IPV. Implications for social work research and practice, including the benefits of using simulation for training IPV service providers, will be discussed.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".