Systematic Suicide Audit: An Enhanced Method to Assess System Gaps and Mobilize Leaders for Prevention
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In Quebec, Canada, several independent processes are in place to investigate cases of death by suicide. An enhanced multidisciplinary audit process was developed to analyze these cases more thoroughly, with the aim of generating recommendations for suicide prevention. A study was undertaken to evaluate the feasibility and implementability of this process. METHODS: The life trajectories of 14 people who died by suicide in Montreal, Canada, in 2016 were reconstructed on the basis of information retrieved by interviewing bereaved relatives and examining coroner investigation files and other records. A multidisciplinary panel that included a representative of families bereaved by suicide then reviewed case summaries to determine unmet needs and service gaps at 3 levels: individual intervention, regional programs, and the provincial health and social services system. RESULTS: The feasibility of the audit process was demonstrated in the context of a public health care system. Thirty-one distinct recommendations were made variably across 13 of the 14 cases reviewed, whereas none had originally been made by the coroner. The recommendations that recurred most often were (1) improve training for professionals and educate the general public regarding depression and substance-related disorders; (2) deploy mobile crisis intervention teams from emergency departments; and (3) provide access to a family physician to all, especially men. CONCLUSION: Although the audit produced novel recommendations and is implementable, there was resistance from physicians and their hospital mortality review committee against this multidisciplinary audit involving families. These concerns could be alleviated by having the process endorsed by provincial authorities.
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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 | 0.153 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".