Can mental health related hospital visits be relied upon for suicide prevention?
Bibliographic record
Abstract
IntroductionEven among people with mental disorders, relatively few die of suicide. However, a large proportion of people dying from suicide have seen a physician in the year before death. This raises the question whether focusing on hospital visits for suicide-related outcomes is a viable suicide prevention strategy. Objectives and ApproachOur objective was to examine whether a hospital visit for a mental disorder or prior suicide attempt preceded suicide death. We requested Saskatchewan’s provincial coroner for records of people dying of suicide in the Saskatoon Health Region catchment area for the years 2012 to 2016. The coroner’s list was linked with hospital and community mental health databases. Patient charts and medical abstracts in both settings were reviewed for risk factors. ResultsThere were 143 suicide deaths in the time period and the yearly incidence was higher in Saskatoon as compared with the national average. Only 38 percent were seen previously in any Saskatoon hospital for a mental disorder (11 percent for a self-harm diagnosis). The chart review confirmed several known psychological and social risk factors. Having a history of depression or psychosis and alcohol and/or drug use were common. Many decedents also had disadvantaged socio-economic backgrounds characterized by vulnerable housing and being on social assistance. Conclusion/ImplicationsWith only 38 percent of decedents being seen in hospital, community-based mental health care and data are important for suicide prevention. Suicide prevention efforts can be aided by facilitating the linkage of community and medical records to better track patients as they move between 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 | 0.005 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".