Hands on!!! Infusing intimate partner violence simulation in nursing education
Bibliographic record
Abstract
Intimate partner violence (IPV) has a 1 in 4 prevalence for women globally. Nursing programs are positioned to prepare students to address IPV screening and brief counselling policy recommendations within curricula. The purpose of this project was to refine the undergraduate nursing curriculum to better facilitate student comfort with and knowledge of IPV screening and intervention using simulation. Methods: We used a 4-item pre/posttest tool to evaluate nursing students’ comfort level with IPV screening and safety planning before and after an IPV simulation with a standardized patient as part of the formative assessment of the simulation. Results: Close to 80% of students (N = 133) reported feeling more comfortable with discussing IPV, screening for IPV, talking to people about IPV, and safety planning after completing the IPV simulation. Conclusion: Infusing IPV screening and intervention simulation into curricula gives students a hands-on opportunity to practice critical trauma-informed skills before encountering a patient exposed to violence. This exposure enhances student comfort with and increases knowledge of screening and intervening with families exposed to IPV and as a result may help to decrease known barriers to IPV screening and intervening post licensure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".