Evaluation of a gatekeeper training program as suicide intervention training for medical students: a randomized controlled trial
Bibliographic record
Abstract
Most individuals who die by suicide have contact with a physician in the year before their death. There are no randomized trials that have evaluated suicide intervention training for medical students or physicians. The objective of this study was to determine the effectiveness of a gatekeeper training program on suicide intervention behavior using Objective Structured Clinical Examinations (OSCEs) in medical students. A randomized controlled trial design was used. Participants were 112 undergraduate medical students at the University of Manitoba. The 2-day Applied Suicide Intervention Skills Training (ASIST) program was completed by half of the participants, according to a stratified block randomization design. Scores on OSCEs and scores on the Suicide Intervention Response Inventory (SIRI-2) were used as objective measures of intervention behaviors. There was a a significant Group-by-Time interaction on OSCE data, demonstrating that medical students who received ASIST performed significantly better than medical students who received training as usual (p<.001). The two groups did not differ significantly from each other on the SIRI-2 (p=.78). ASIST training improved the ability of medical students to detect and intervene with a standardized suicidal patient as assessed by OSCEs, compared to medical school training as usual. This study provides support for ASIST training for medical students to develop skills in recognition and management of suicidal patients.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".