Suicide risk assessment and intervention in people with mental illness
Bibliographic record
Abstract
Suicide is the 15th most common cause of death worldwide. Although relatively uncommon in the general population, suicide rates are much higher in people with mental health problems. Clinicians often have to assess and manage suicide risk. Risk assessment is challenging for several reasons, not least because conventional approaches to risk assessment rely on patient self reporting and suicidal patients may wish to conceal their plans. Accurate methods of predicting suicide therefore remain elusive and are actively being studied. Novel approaches to risk assessment have shown promise, including empirically derived tools and implicit association tests. Service provision for suicidal patients is often substandard, particularly at times of highest need, such as after discharge from hospital or the emergency department. Although several drug based and psychotherapy based treatments exist, the best approaches to reducing the risk of suicide are still unclear. Some of the most compelling evidence supports long established treatments such as lithium and cognitive behavioral therapy. Emerging options include ketamine and internet based psychotherapies. This review summarizes the current science in suicide risk assessment and provides an overview of the interventions shown to reduce the risk of suicide, with a focus on the clinical management of people with mental disorders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".