Suicide methods and severe mental illness: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: People with severe mental illness (SMI) have a higher risk of suicide compared with the general population. However, variations in suicide methods between people with different SMIs have not been examined. The aim of this pre-registered (PROSPERO CRD42022351748) systematic review was to pool the odds of people with SMI who die by suicide versus those with no SMI, stratified by suicide method. METHODS: Searches were conducted on December 11, 2023 across PubMed, PsycInfo, CINAHL, and Embase. Eligible studies were those that reported suicide deaths stratified by SMI and suicide methods. Studies were pooled in a random-effects meta-analysis, and risk of bias was measured by the Joanna Briggs Institute checklist. RESULTS: After screening, 12 studies were eligible (n = 380,523). Compared with those with no SMI, people with schizophrenia had 3.38× higher odds of jumping from heights (95% CI: 2.08-5.50), 1.93× higher odds of drowning (95% CI: 1.50-2.48). People with bipolar disorder also had 3.2× higher odds of jumping from heights (95% CI: 2.70-3.78). Finally, people with major depression had 3.11× higher odds of drug overdose (95% CI: 1.53-6.31), 2.11× higher odds of jumping from heights (95% CI: 1.93-2.31), and 2.33× lower odds of dying by firearms (OR = 0.43, 95% CI: 0.33-0.56). No studies were classified as high risk of bias, and no outcomes had high levels of imprecision or indirectness. CONCLUSION: These findings could inform lethal means counselling practices in this population. Additionally individual, clinical, community and public health interventions for people with SMI should prioritise, where feasible, means restriction including access to heights or drugs to overdose.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".