Effectiveness of a Web-Based Virtual Simulation to Train Nursing Students in Suicide Risk Assessment: Randomized Controlled Investigation
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».