Exploring the scope and structure of suicide capability
Bibliographic record
Abstract
OBJECTIVE: Recent theories of suicide suggest that a construct called "capability for suicide" facilitates the progression from suicidal thoughts to attempts. Various measures of capability have been developed to assess different parts of the construct, but studies report inconsistent findings regarding reliability, validity, and structure. The present study pooled items from multiple measures to identify distinct, reliable, and valid domains of suicide capability. METHOD: We administered items from several suicide capability measures to an online sample of US adults (n = 387), and utilized exploratory factor analysis to identify distinct domains of capability. We then examined the internal consistencies of and intercorrelations among these domains, as well as their associations with suicide attempts. RESULTS: Findings identified three domains of suicide capability: fearlessness about death, practical capability, and pain tolerance. These domains were internally consistent (αs = 0.80-0.92), and relatively independent from one another (intercorrelations = 0.15-0.35). Finally, each of these domains was moderately elevated among attempters compared to ideators (although only fearlessness about death and practical capability offered unique information about attempter status). CONCLUSIONS: Findings suggest that fearlessness about death, practical capability, and pain tolerance can be measured reliably, and may be relevant for understanding which ideators make attempts.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".