Harnessing Innovative Technologies to Train Nurses in Suicide Safety Planning With Hospital Patients: Formative Acceptability Evaluation of an eLearning Continuing Education Training
Notice bibliographique
Résumé
BACKGROUND: Suicide is the 12th leading cause of death in the United States. Health care provider training is a top research priority identified by the National Action Alliance for Suicide Prevention; however, evidence-based approaches that target skill building are resource intensive and difficult to implement. Novel computer technologies harnessing artificial intelligence are now available, which hold promise for increasing the feasibility of providing trainees opportunities across a range of continuing education contexts to engage in skills practice with constructive feedback on performance. OBJECTIVE: This pilot study aims to evaluate the feasibility and acceptability of an eLearning training in suicide safety planning among nurses serving patients admitted to a US level 1 trauma center for acute or intensive care. The training included a didactic portion with demonstration, practice of microcounseling skills with a web-based virtual patient (Client Bot Emily), role-play with a patient actor, and automated coding and feedback on general counseling skills based on the role-play via a web-based platform (Lyssn Advisor). Secondarily, we examined learning outcomes of knowledge, confidence, and skills in suicide safety planning descriptively. METHODS: Acute and intensive care nurses were recruited between November 1, 2021, and May 31, 2022, to participate in a formative evaluation using pretraining, posttraining, and 6-month follow-up surveys, as well as observation of the nurses' performance in delivering suicide safety planning via standardized patient role-plays over 6 months and rated using the Safety Plan Intervention Rating Scale. Nurses completed the System Usability Scale after interacting with Client Bot Emily and reviewing general counseling scores based on their role-play via Lyssn Advisor. RESULTS: A total of 18 nurses participated in the study; the majority identified as female (n=17, 94%) and White (n=13, 72%). Of the 17 nurses who started the training, 82% (n=14) completed it. On average, the System Usability Scale score for Client Bot Emily was 70.3 (SD 19.7) and for Lyssn Advisor was 65.4 (SD 16.3). On average, nurses endorsed a good bit of knowledge (mean 3.1, SD 0.5) and confidence (mean 2.9, SD 0.5) after the training. After completing the training, none of the nurses scored above the expert-derived cutoff for proficiency on the Safety Plan Intervention Rating Scale (≥14); however, on average, nurses were above the cutoffs for general counseling skills per Lyssn Advisor (empathy: mean 4.1, SD 0.6; collaboration: mean 3.6, SD 0.7). CONCLUSIONS: Findings suggest the completion of the training activities and use of novel technologies within this context are feasible. Technologic modifications may enhance the training acceptability and utility, such as increasing the virtual patient conversational abilities and adding automated coding capability for specific suicide safety planning skills. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/33695.
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Comment cette classification a été obtenuedéplier
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 | 0,005 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».