Implementing a Suicide Audit in Montreal: Taking Suicide Review Further to Make Concrete Recommendations for Suicide Prevention
Bibliographic record
Abstract
OBJECTIVE: A suicide audit was pilot implemented in order to generate recommendations on how to improve suicide prevention. METHODS: Thirty-nine consecutive suicides that occurred in Montreal, Canada from January to October 2016 were audited. A retrospective analysis of their life trajectory and service utilization was conducted using the psychological autopsy method, which included interviewing suicide-bereaved survivors and examining health and social services records and the coroner's investigation file. A psychosocial and service utilization profile was drawn for each decedent. A multidisciplinary panel reviewed each case summary to identify gaps in terms of individual intervention, provincial public health and social services, and regional programs. RESULTS: Five main suicide prevention recommendations were made to prevent similar suicides: (1) deploy mobile crisis intervention teams (short-term, high-intensity, home-based treatment) in hospital emergency departments; (2) train primary and specialized mental health care professionals to screen for and manage substance use disorders; and (3) implement public awareness campaigns to encourage help seeking for depression and substance use disorders; (4) access for all, regardless of age, to an effective psychosis treatment program; and (5) provide universal access to a general practitioner, especially for men. CONCLUSIONS: The suicide audit procedure was implemented effectively and targeted recommendations were generated to prevent similar suicides. However, resistance from medical and hospital quality boards arose during the process, though these could be allayed if regional and provincial authorities actively endorsed the multidisciplinary and multi stakeholders suicide audit process. HighlightsA bottom-up approach to generate recommendations for suicide prevention.Implementation was challenging with resistance to our interdisciplinary approach.The audit needs the support of the regional health department to lift barriers.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".