Bibliographic record
Abstract
Elevated impulsivity is thought to facilitate the transition from suicidal thoughts to suicidal behavior. Therefore, impulsivity should distinguish those who have attempted suicide (attempters) from those who have only considered suicide (ideators-only). This hypothesis was examined in three large nonclinical samples: (1) 2,011 military recruits, (2) 1,296 college students, and (3) 399 high school students. In sample 1, contrary to traditional models of suicide risk, a unidimensional measure of impulsivity failed to distinguish attempters from ideators-only. In samples 2 and 3, which were administered a multidimensional measure of impulsivity (i.e., the UPPS impulsive behavior scale; Whiteside & Lynam, 2001), different impulsivity-related traits characterized attempters and ideators-only. Whereas both attempters and ideators-only exhibited high urgency (the tendency to act impulsive in the face of negative emotions), only attempters exhibited poor premeditation (a diminished ability to think through the consequences of one's actions). Neither attempters nor ideators-only exhibited high sensation seeking or lack of perseverance. Future research should continue to distinguish impulsivity-related traits that predict suicide ideation from those that predict suicide attempts, and models of suicide risk should be revised accordingly.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".