Understanding the impact of involuntary discoveries of nonsuicidal self-injury: a thematic analysis
Bibliographic record
Abstract
Growing research has examined instances of voluntarily disclosed nonsuicidal self-injury (NSSI), including how people with lived experience are impacted when they choose to share their NSSI with others. Voluntary disclosure, however, represents just one way that NSSI experiences become known to others; NSSI can also be discovered involuntarily, yet little to no research has explored the impact of these experiences. To understand the impact of these Involuntary Discovery Experiences (IDEs) the present study recruited 139 university students (Mage = 19.13, SD = 2.12; nfemale = 121) with lived experience of NSSI and who reported having a past IDE. Participants took part in an online study involving a series of open-ended questions concerning their past IDEs. A thematic analysis of their responses pointed to three overarching psychological impacts of IDEs: I felt Stigmatized and Marginalized, Things did not go well, and I No Longer felt Alone in my experience. These findings offer initial insights into the ways people with lived experience of NSSI may be impacted by IDEs and point to several new important research avenues. The current findings also suggest that clinicians may need to ask clients about any potential IDEs and their impact in order to best support clients who self-injure.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".