An Examination of Disclosure of Nonsuicidal Self‐injury among University Students
Bibliographic record
Abstract
ABSTRACT Despite the widespread prevalence of nonsuicidal self‐injury (NSSI) among community‐based samples, little is known about which self‐injurers disclose their NSSI or the factors that promote disclosure among self‐injurers. To address this gap in the literature, we examined whether disclosers could be differentiated from nondisclosers on the basis of NSSI characteristics (e.g. frequency of NSSI and severity of NSSI), NSSI motivations (e.g. interpersonal and intrapersonal motivations) and psychosocial factors (e.g. suicidal ideation and self‐esteem). Participants consisted of a large sample of 268 self‐injuring undergraduate students ( M age = 19.07 years, 70.3% women) at a Canadian university. Results indicated that 57% of self‐injurers had never disclosed their NSSI to anyone. Self‐injurers were most likely to disclose to peers and romantic partners. Logistic regression analyses revealed that pain during NSSI, severity of NSSI, interpersonal motivations for engaging in NSSI, higher suicidal ideation and higher friendship quality were all associated with a greater likelihood of NSSI disclosure. Our findings suggest that individuals with severe NSSI and suicidal ideation may be more likely to disclose. Moreover, our findings underscore the importance of equipping friends and romantic partners with effective responses to NSSI disclosures to promote more formal help‐seeking behaviours among self‐injurers. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".