An evaluation of SimVoice mental health de-escalation training
Bibliographic record
Abstract
Systemic changes, such as the deinstitutionalization of mental health care, have increased the likelihood that people with acute mental health symptoms encounter the police. Given this, greater attention is being paid to mental health training for police officers in Canada. The current study presents a preliminary evaluation of SimVoice, a training tool that was designed to enhance the realism of de-escalation training for officers who may encounter individuals experiencing auditory hallucinations. Survey responses from trainees and trainers who took part in SimVoice training suggest the training is viewed very positively. Trainers felt that SimVoice was easy to use and contributed to realistic training, and they unanimously indicated they will continue using SimVoice as a training tool. Trainers and trainees both felt that the training was useful for developing general and specific knowledge and skills that would help officers more effectively manage encounters with people in crisis. Survey respondents also identified limitations when using SimVoice, along with suggestions for improving its use in training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".