Effectiveness of a Web-Based Virtual Simulation to Train Nursing Students in Suicide Risk Assessment: Randomized Controlled Investigation
Bibliographic record
Abstract
Background: Suicide is a leading cause of preventable death worldwide. Nurses play a critical role in suicide prevention; yet, they face significant obstacles. Improving the evaluation and management of patients at risk of suicide requires innovative training techniques that safely and effectively enhance nursing students' skills, knowledge, and confidence. Virtual simulation (VS) based training can be particularly effective because it allows interaction with patients without the risk of causing harm. Objective: The purpose of this study was to evaluate the pedagogical effectiveness of a novel VS tool featuring a fully automated and emotionally reactive virtual patient by assessing its ability to assist nursing students in learning suicide risk assessment. VS also included an online group debriefing, co-run by a nurse and a medical teacher. Methods: A randomized controlled investigation was conducted with 68 first-year nursing students recruited from nursing schools offline and online. They were divided into a control group receiving teaching as usual (TAU) and an intervention group receiving TAU plus VS. The intervention was purely web-based and unblinded. Outcomes were self-assessed through questionnaires using Kirkpatrick Training Evaluation Model, which focuses on knowledge, skills, confidence, empathy, and satisfaction among students. Results: The VS group exhibited significantly higher confidence (3 points of increase after TAU vs 10.6 points of increase after VS, B=7.2; SE 2.5; t111.5=2.8; P=.006) and a marginally enhanced ability to respond appropriately to suicidal thoughts (1.6 points of improvement after TAU vs 6.4 points of improvement after VS, B=-4.5; SE 2.5; t119.5=-1.8; P=.08) compared with the control group. However, there were no significant differences in knowledge acquisition or the general level of empathy. Satisfaction with VS was high, particularly regarding the authenticity of the virtual patient. Authenticity was perceived as greater when emotional prosody was included with facial emotions. Conclusions: The use of VS demonstrated promising results in enhancing nursing students' confidence in detecting suicide risk and their skills in counseling individuals experiencing a suicide crisis, suggesting its incorporation into routine teaching methods. Further research is needed to explore its long-term benefits for students and its impact on patient outcomes.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".