Affective instability and impulsivity predict nonsuicidal self-injury in the general population: a longitudinal analysis
Bibliographic record
Abstract
BACKGROUND: Impulsivity and affective instability are related traits known to be associated with nonsuicidal self-injury, although few longitudinal studies have examined this relationship. The purpose of this study was to determine if impulsivity and affective instability predict future nonsuicidal self-injury in the general population while accounting for the overlap between these traits. METHODS: Logistic regression analyses were conducted on data from 2344 participants who completed an 18-month follow-up of the 2000 British National Psychiatric Morbidity Survey. Affective instability and impulsivity were assessed at baseline with the Structured Clinical Interview for DSM-IV Axis II Personality Disorders. Nonsuicidal self-injury was assessed at baseline and follow-up during semi-structured interviews. RESULTS: Affective instability and impulsivity predicted the onset of nonsuicidal self-injury during the follow-up period. Affective instability, but not impulsivity, predicted the continuation of nonsuicidal self-injury during the follow-up period. Affective instability accounted for part of the relationship between impulsivity and nonsuicidal self-injury. CONCLUSIONS: Affective instability and impulsivity are important predictors of nonsuicidal self-injury in the general population. It may be more useful to target affective instability over impulsivity for the treatment of nonsuicidal self-injury.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".