Simulation training in suicide risk assessment and intervention: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Suicide is a major cause of preventable death worldwide. Adequate training in risk assessment and intervention is key to suicide prevention. The use of simulation (role plays, simulated patients, virtual reality…) for practical training is a promising tool in mental health. The purpose of this study was to assess the effectiveness of simulation training in suicide risk assessment and intervention for healthcare professionals and gatekeepers. METHODS: We conducted a systematic review in Medline and PsycINFO up to 31 July 2021 of randomized controlled trials (RCTs), non-randomized controlled trials, and pre/post-test studies. RCTs were furthermore included in a meta-analysis. We assessed the methodological quality of all studies with the Medical Education Research Study Quality Instrument, and the Cochrane Risk of Bias tool 2.0 for RCTs. Primary outcomes were changes in Kirkpatrick criteria: attitudes, skills, knowledge, behaviors, and patient outcomes. RESULTS: We included 96 articles representing 43,656 participants. Most pre/post-test (n = 65) and non-randomized controlled (n = 14) studies showed significant improvement in attitudes, skills, knowledge, and behaviors. The meta-analysis of 11 RCTs showed positive changes in attitudes immediately after training and at 2-4 months post-training; in self-perceived skills at 6 months post-training; but not in factual knowledge. Studies assessing benefits for patients are still limited. CONCLUSIONS: The heterogeneity of methodological designs, interventions, and trained populations combined with a limited number of RCTs and studies on patients' outcomes limit the strength of the evidence. However, preliminary findings suggest that simulation is promising for practical training in suicidal crisis intervention and should be further studied.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".