Testing Associations Between Impulsivity Facets and Suicidal Behaviour Among Young Adults
Bibliographic record
Abstract
Suicide is the second leading cause of death among young adults in Canada (Public Health Agency of Canada, 2019). Impulsivity is one of the most widely studied correlates of suicidal behaviours. However, most studies to date have treated impulsivity as a homogenous construct, masking potential unique associations between specific subdomains of impulsivity and suicide-related outcomes. Further, sex differences in suicidal behaviour rates have been identified, such that females have greater rates of non-lethal suicide attempts than males; but the processes that facilitate these differences are not yet well understood (Polewka et al., 2004; Mościcki, 1994). To address these gaps, I recruited 117 young adults (18-24 years old) who endorsed suicidal thoughts and/or behaviours within the past year. All participants completed a series of questionnaires and behavioural tasks that measured different state and trait impulsivity facets. The study was completed while on Zoom with a trained lab member. Using a series of negative binomial hurdle models, I found that among ideators, lack of premeditation (acting without thinking), was significantly associated with an increased likelihood of endorsing a previous suicidal behaviour (aborted, interrupted, and suicide attempts), and the strength of this association was not significantly moderated by sex. Moreover, in an exploratory analysis using a series of ordinal regressions, I found that deficits in prepotent response inhibition were significantly associated with more distal suicidal behaviours (i.e., lifetime) when compared to past year and past month suicidal behaviours. This work has significant implications both theoretically and clinically, and ultimately aims to inform enhanced suicide prevention measures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".