Understanding experiences of disclosing and receiving disclosures of nonsuicidal self-injury amongst peers in university: A qualitative investigation
Bibliographic record
Abstract
Nonsuicidal self-injury (NSSI) is a frequently occurring mental health concern among emerging adults in university, but one that is often concealed. Given that the disclosure of NSSI can provide opportunities to receive support, promoting positive disclosure experiences for students is important. However, the experiences of disclosing for both disclosers and recipients are not well understood. In the present study, we examined experiences leading up to, during, and following disclosures from students with lived experience giving and/or receiving a peer disclosure of NSSI. Semi-structured interviews were conducted with 20 undergraduate students (Mage = 19.95, 80% female), and reflexive thematic analysis was used. Four shared themes were identified : 1) The choice to disclose is a social cost-benefit analysis, in which context and past experiences matter, 2) Individuals seek emotional and practical support from their peers via disclosure, 3) Supportive responding constitutes care, empathy, and non-judgment, and 4) Disclosure can lead to awareness, change, and growth. One theme was unique to recipients: 5) Disclosure can be an overwhelming process, and many recipients feel ill-equipped to respond. Findings can be used to inform mental health literacy efforts for students on university campuses.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".