The Disclosure of Non-Suicidal Self-Injury: A Qualitative Dyadic Study
Bibliographic record
Abstract
Objectives : The disclosure of non-suicidal self-injury (NSSI) can be critical in facilitating recovery and preventing future NSSI, thus understanding the disclosure process is important. However, the disclosure of NSSI has traditionally been studied through an individual perspective, which falls short of capturing its true interpersonal nature. To address this gap, the current study aimed to qualitatively explore the experience of NSSI disclosure from both the discloser and their recipient’s perspectives. Methods : We conducted semi-structured interviews with 12 dyads ( N = 24, age = 18–45, 78% female) and analysed data using the framework method. Results : Seven themes were present across three stages of the disclosure process: the antecedent factors (relational factors, function of disclosures, anticipated reactions), the disclosure event (social reaction, emotional experience), and the perceived outcome of the disclosure (relational impact, intrapersonal growth). Conclusion : The findings position NSSI disclosures as a relational process wherein disclosers and recipients continuously draw on interpersonal factors to interpret and find meaning in their experiences with the disclosure. The insights from the current study offer important clinical implications for improving support in informal contexts, extend theoretical models of disclosure by accounting for dyadic processes, and demonstrate the methodological value of capturing the disclosure experience from a dyadic perspective.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".