Maternal Deaths by Suicide and Drug Overdose in Two Canadian Provinces; Retrospective Review
Bibliographic record
Abstract
OBJECTIVES: To identify and review factors associated with maternal deaths by suicide and drug overdose in the Canadian Coroner and Medical Examiners Database, from 2017 to 2019. METHODS: We identified potential maternal deaths in Ontario and British Columbia by searching the Canadian Coroner and Medical Examiners Database narratives of deaths to females 10 to 60 years old for pregnancy-related terms. Identified narratives were then qualitatively reviewed in quadruplicate to determine if they were maternal deaths by suicide or drug overdose, and to extract information on maternal characteristics, the manner of death, and factors associated with each death. RESULTS: Of the 90 deaths identified in this study, 15 (16.7%) were due to suicide and 20 (22.2%) were due to a drug overdose. These deaths occurred in women of varying ages and across the pregnancy-postpartum period. Among the suicides, 10 were by hanging, and among the overdose-related deaths, 15 had fentanyl detected. Notably, 13 (37.1%) of the 35 deaths to suicide or drug overdose occurred beyond 42 days after pregnancy, 19 (54.3%) followed a miscarriage or induced abortion, and in 23 (65.7%) there was an established history of mental health illness. Substance use disorders were documented in 4 of the 15 suicides (26.7%), and 18 of the 20 overdose-related deaths (90.0%). CONCLUSIONS: Suicide and drug overdose may contribute more to maternal deaths in Canada than previously realized. Programs are needed to identify women at risk of these outcomes and to intervene during pregnancy and beyond the conventional postpartum period.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.027 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".