Harnessing Innovative Technologies to Train Nurses in Suicide Safety Planning With Hospital Patients: Formative Acceptability Evaluation of an eLearning Continuing Education Training
Bibliographic record
Abstract
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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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".