Peer reactions to non-suicidal self-injury disclosures: a thematic analysis
Bibliographic record
Abstract
Emerging adults who engage in non-suicidal self-injury (NSSI) tend to share such experiences with peers. Thus, peers may represent a vital avenue for accessing recovery support and further help (e.g. encouraging professional help-seeking). With little known about how university students respond to NSSI disclosures from peers, the present study attempted to examine these experiences from the vantage point of disclosure recipients. Responses were gathered from a sample of undergraduate students who received an NSSI disclosure from a friend. Specifically, 104, primarily white female, emerging adults (M = 18.29 years of age, SD = .80) were recruited from a Canadian university. Demographics and open-ended questions asking about experiences receiving an NSSI disclosure from a friend were collected via online survey. Responses to the open-ended qualitative questions were examined using a thematic analysis. Responses fell broadly into four domains (intense emotional reactions, supportive responding, impact on peer relationship, and perceived insight about NSSI), which were each composed of two to four themes. Overall, findings point to several implications for researchers (e.g. developing accessible resources for peers of those who self-injure) and counselors (e.g. helping people who self-injure to understand the reactions of their peers).
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".