Coroners' records on suicide mortality in Montréal: limitations and implications in suicide prevention strategies
Bibliographic record
Abstract
INTRODUCTION: In Montréal, the characteristics of suicide cases may vary between different areas. The information collected by coroners during their investigations of suicides could be used to support local suicide-prevention planning actions. METHODS: This study analyzes all coroners' records on suicide in Montréal from 2007 to 2009 to (1) determine the usefulness of the data available; (2) develop a profile of cases; (3) examine local differences by comparing two areas, one with the highest suicide rate and the other with the lowest. RESULTS: The data collected revealed the lack of a systematic, standardized procedure for recording information about deaths by suicide. The rates of missing data varied, but were very high for antecedents of suicide attempts and recent events that could have precipitated the suicide. We observed differences in the characteristics of suicide cases according to area of residence. CONCLUSION: By adopting a standardized procedure for collecting information on cases of suicide, coroners could provide local decision makers with a more accurate portrait of the people who die by suicide in their area. Local adjustments may improve suicide prevention strategies.
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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 | 0.024 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".