Bibliographic record
Abstract
PURPOSE OF REVIEW: Suicide is a major cause of mortality accounting for nearly 1 million deaths globally per year. Suicide occurs throughout the lifespan; therefore, large epidemiological samples are needed to identify patterns in suicide death. This review examines emerging evidence relating to risk and protective factors as well as preventive measures for suicide. RECENT FINDINGS: The global financial crisis, natural disasters, air pollution and second-hand smoke have all been associated with increased suicide rates. At an individual level, past self-harm, parental loss or separation and younger age relative to classmates all confer risk. There is mixed evidence for religious affiliation and lithium levels in drinking water as protective factors. Means restriction strategies including barriers at suicide hotspots, firearms restrictions and limiting access to both pesticides and charcoal have all prevented suicide. Other interventions with recent evidence include improvements in mental health systems, selective serotonin reuptake inhibitor (SSRI) and lithium treatment in youth and mental health awareness in schools. SUMMARY: The evidence for risk/protective factors for suicide continues to grow and, more importantly, numerous prevention efforts continue to demonstrate positive outcomes. Public policy experts should attend to the environmental and social determinants of health when devising suicide prevention programs, and the evidence-based prevention strategies identified here should be implemented more broadly.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".