Population‐based analysis of health care contacts among suicide decedents: identifying opportunities for more targeted suicide prevention strategies
Bibliographic record
Abstract
The objective of this study was to detail the nature and correlates of mental health and non-mental health care contacts prior to suicide death. We conducted a systematic extraction of data from records at the Office of the Chief Coroner of Ontario of each person who died by suicide in the city of Toronto from 1998 to 2011. Data on 2,835 suicide deaths were linked with provincial health administrative data to identify health care contacts during the 12 months prior to suicide. Sub-populations of suicide decedents based on the presence and type of mental health care contact were described and compared across socio-demographic, clinical and suicide-specific variables. Time periods from last mental health contact to date of death were calculated and a Cox proportional hazards model examined covariates. Among suicide decedents, 91.7% had some type of past-year health care contact prior to death, 66.4% had a mental health care contact, and 25.3% had only non-mental health contacts. The most common type of mental health contact was an outpatient primary care visit (54.0%), followed by an outpatient psychiatric visit (39.8%), an emergency department visit (31.1%), and a psychiatric hospitalization (21.0%). The median time from last mental health contact to death was 18 days (interquartile range 5-63). Mental health contact was significantly associated with female gender, age 25-64, absence of a psychosocial stressor, diagnosis of schizophrenia or bipolar disorder, past suicide attempt, self-poisoning method and absence of a suicide note. Significant differences between sub-populations of suicide decedents based on the presence and nature of their health care contacts suggest the need for targeting of community and clinical-based suicide prevention strategies. The predominance of ambulatory mental health care contacts, often close to the time of death, reinforce the importance of concentrating efforts on embedding risk assessment and care pathways into all routine primary and specialty clinical care, and not only acute care settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".