Suicide risk assessment and intervention in people with mental illness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it