The Perceived Effectiveness of a Suicide Assessment Virtual Simulation Module for Undergraduate Nursing Students
Bibliographic record
Abstract
Background Nursing students identify a lack of knowledge and clinical experience in the assessment of suicidal risk, which can negatively impact the care provided to individuals experiencing a mental health crisis. The purpose of this study was to explore the perceived effectiveness of a Suicidal Ideation – Assessment of Risk virtual simulation module for undergraduate nursing students. Method A mixed methods explanatory sequential design study was conducted with third-year nursing students (N = 130) enrolled in a mental health nursing course from an Ontario-based university. The effectiveness of the virtual simulation was evaluated using the Simulation Effectiveness Tool-Modified (SET-M), followed by semi-structured individual interviews (n = 8). Results The virtual simulation was perceived to be effective. Due to the sensitive topic of suicide, this study validated the importance of adhering to the Healthcare Standards of Best Practice 2021, specifically a structured debrief with a skilled facilitator. Qualitative findings identified increased learning, preparedness, confidence, knowledge, critical reflection, and decreased anxiety. Conclusion This virtual simulation module reinforced the importance of providing application-based mental health assessment experiences prior to entering clinical practice.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".