Identification of Risk Factors for Suicide and Insights for Developing Suicide Prevention Technologies: A Systematic Review and Meta-Analysis
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 a termite that engulfs close to seven hundred thousand people worldwide each year. Existing work on risk factors that predict suicide lacks statistical associations, does not consider most countries, and has a wide range of risk factor domains. The goal of this systematic review and meta-analysis is to enhance our current understanding of suicidality by identifying risk factors that are most strongly associated with suicide and their impact on developing technological interventions for suicide prevention. A search strategy was carried out on four databases: (1) PsycINFO, (2) IEEE Xplore, (3) the ACM Digital Library, and (4) PubMed, and twenty-five studies were included based on the inclusion criteria. Factors statistically associated with suicide are any diagnosed mental disorder, adverse life events, past suicide attempts, low education level, loneliness or high levels of isolation, bipolar disorder, depression, multiple chronic health conditions, family history of suicide, sexual trauma, and being female. Domain-wise, comorbid disorders, and behavior-related risk factors are most strongly associated with suicide. We present a new hierarchical model of risk factors for suicide that advances our understanding of suicide and its causes. Finally, we present open research directions and considerations for developing suicide prevention technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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