Preventing Suicidal Behaviour in a General Hospital Psychiatric Service: Priorities for Programming
Bibliographic record
Abstract
PURPOSE: General hospital psychiatric services are able to provide leadership and coordinate the development of suicide prevention programs for individuals serviced in general hospital settings. We completed this literature review to suggest priorities for programming. METHODS: Our procedure was to update the review by Gunnell and Frankel that guided priorities for Health of the Nation, the national suicide prevention strategy in the UK. We completed a search, using the terms suicide prevention and control, of all English-language research and clinical trials conducted between January 1, 1994, and May 1, 2004. RESULTS: We identified 82 papers. Of these, 48 were excluded and the remaining 34 were grouped by secondary care setting categories. We found no articles on screening tools for predicting risk of suicide, 16 articles on interventions for individuals with suicidal behaviour, 14 articles on the treatment of major psychiatric disorders, 1 article and 1 published abstract on discharge from hospital, and 2 articles on reducing access to means. CONCLUSIONS: Based on a review of each category, we make several program and policy recommendations, including regularly updating clinical assessment skills, using guidelines for assessment of patients following a suicide attempt, assessing the risk of suicide 24 to 48 hours before discharge from hospital, and incorporating education about reducing access to means into routine psychiatric care.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".