Correlates of disclosure of non-suicidal self-injury amongst Australian university students
Bibliographic record
Abstract
Purpose Non-suicidal self-injury (NSSI) is associated with psychological disorders and suicidal thoughts and behaviours; disclosure of NSSI can serve as a catalyst for help-seeking and self-advocacy amongst people who have self-injured. This study aims to identify the socio-demographic, NSSI-related, socio-cognitive and socio-emotional correlates of NSSI disclosure. Given elevated rates of NSSI amongst university students, this study aimed to investigate these factors amongst this population. Design/methodology/approach Australian university students (n = 573) completed online surveys; 80.2% had previously disclosed self-injury. Findings NSSI disclosure was associated with having a mental illness diagnosis, intrapersonal NSSI functions, specifically marking distress and anti-dissociation, having physical scars from NSSI, greater perceived impact of NSSI, less expectation that NSSI would result in communication and greater social support from friends and significant others. Originality/value Expanding on previous works in the area, this study incorporated cognitions about NSSI. The ways in which individuals think about the noticeability and impact of their NSSI, and the potential to gain support, are associated with the decision to disclose self-injury. Addressing the way individuals with lived experience consolidate these considerations could facilitate their agency in whether to disclose their NSSI and highlight considerations for health-care professionals working with clients who have lived experience of NSSI.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".