Impulsivity and nonsuicidal self‐injury: A longitudinal examination among emerging adults
Bibliographic record
Abstract
INTRODUCTION: Nonsuicidal self-injury (e.g., self-cutting without lethal intent) is a widely occurring behavior among adolescents and emerging adults. Heightened impulsivity during the adolescent and emerging adult years may contribute to an increased risk for nonsuicidal self-injury onset and engagement during these developmental periods; however, longitudinal research on impulsivity and nonsuicidal self-injury among young persons is lacking. METHOD: To extend previous research, and elucidate the direction of effects between impulsivity and nonsuicidal self-injury, 782 emerging adults (75% female) completed assessments of impulsivity and nonsuicidal self-injury annually for three years. RESULTS: Path analysis revealed a bidirectional relation, such that higher impulsivity predicted greater nonsuicidal self-injury frequency over time, and greater nonsuicidal self-injury frequency predicted higher impulsivity over time, even after taking into account past scores on these variables as well as other participant factors (e.g., age, sex, depressive symptoms, anxiety). Gender did not moderate the pattern of associations. CONCLUSIONS: The present findings indicate that impulsivity may not only lead to nonsuicidal self-injury, but that frequent engagement in nonsuicidal self-injury may undermine regulatory skill development and lead to greater impulsivity over time during the emerging adult years.
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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 | 0.002 |
| 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 | 0.001 |
| 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".