Nonsuicidal Self‐injury in Asian Versus Caucasian University Students: Who, How, and Why?
Bibliographic record
Abstract
The correlates of nonsuicidal self-injury (NSSI) among Asian and Caucasian university students; differences in the rates, frequency, forms, severity, and emotional contexts of NSSI among self-injuring students; and whether Asian students who are highly oriented toward Asian culture differed from those less oriented toward Asian culture in NSSI characteristics were investigated. University students (N = 931), including 360 Caucasian students (n = 95, 26.4%, with a history of ≥ 1 episode of NSSI) and 571 Asian students (n = 107, 18.7%, with a history of NSSI), completed questionnaires assessing NSSI, acculturation, and putative risk factors for NSSI. Caucasian students were more likely to report NSSI, particularly cutting behavior, self-injured with greater frequency and versatility, and reported greater increases in positively valenced, high arousal emotions following NSSI, compared to Asian students. Among Asian students, obsessive-compulsive symptoms, experiential avoidance, and anger suppression increased the likelihood of reporting a history of NSSI. Among Caucasian students, lack of emotional clarity and anger suppression increased likelihood of NSSI. Finally, some tentative findings suggested potentially important differences in rates and frequency of NSSI among Asian students who were highly oriented toward Asian culture compared with those less oriented toward Asian culture.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".