Recent advances in differentiating suicide attempters from suicide ideators
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article summarizes findings from recent studies (published since 2015) examining differences between suicide attempters and suicide ideators. RECENT FINDINGS: Converging evidence suggests that the capability to attempt suicide (e.g., acquired capability, painful and provocative experiences, high tolerance for pain and distress) is higher in suicide attempters than suicide ideators. Other psychosocial and biological differences have also been identified but require replication. SUMMARY: Recent literature reviews find that traditional risk factors for suicide - such as depression, hopelessness, most psychiatric disorders, and even impulsivity - robustly predict suicide ideation but poorly predict suicide attempts among ideators. To address this knowledge gap, studies are increasingly employing an ideation-to-action framework. This framework views the development of suicide ideation and the progression from ideation to potentially lethal attempts as distinct processes with distinct explanations and predictors. Converging evidence suggests that factors associated with diminished fear of pain, injury, and death can increase one's capability to attempt suicide and facilitate the progression from suicidal thoughts to suicidal acts. Recent studies have also identified other variables that may differentiate attempters from ideators, but these require replication. Theories of suicide positioned within the ideation-to-action framework provide testable and promising hypotheses about the progression from ideation to attempts. These include the Interpersonal Theory, Integrated Motivational-Volitional Model, and Three-Step Theory.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".