Prevention of alcohol-related suicide: a rapid review
Bibliographic record
Abstract
Suicide remains a leading cause of death worldwide, with an estimated 700,000 suicide deaths per year. The World Health Organization identifies reducing alcohol use as one component of comprehensive approaches to suicide prevention. This paper conducted a rapid review of the evidence on alcohol-related suicide prevention interventions. PubMed, Embase and Web of Science were searched for articles related to alcohol, suicide, prevention, and policies, published between 1990 and 2020. 5293 articles were identified; after deduplication, 2567 studies were screened at the title and abstract level. 402 articles underwent full-text review. 69 articles were ultimately included and underwent data extraction. Interventions were categorized as policy interventions, community-based interventions, and clinical interventions. While there is evidence that policy interventions targeting alcohol may be associated with lower suicide rates, more evidence using stronger study designs is needed. The evidence for community interventions was mixed and supported the need for further research on these types of interventions. Pharmaceutical and therapy-based clinical interventions also showed some promise, with more research needed. Overall, despite evidence of alcohol’s role in suicide attempts and deaths, few interventions have been developed with the purpose of addressing alcohol-related suicide. More research is needed to identify effective interventions to prevent alcohol-related suicide.
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.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".