Why Are Individuals Who Have Not Disclosed Self-Injury Comfortable Discussing Their Experiences in Research?
Bibliographic record
Abstract
ABSTRACT: Not everyone who shares their lived experience of nonsuicidal self-injury (NSSI) in research has disclosed this previously outside of a research context. Our objective was to identify reasons people who have not previously disclosed their NSSI felt comfortable discussing their self-injury in research contexts. The sample consisted of 70 individuals with lived experience of self-injury who had not previously disclosed this experience outside of research (Mage = 23.04 years, SD = 5.90; 75.70% women). Using content analysis of open-ended responses, we identified three reasons participants felt comfortable discussing their NSSI in research. Most commonly, participants did not anticipate negative consequences discussing their NSSI due to the way the research was conducted (e.g., confidentiality). Second, participants valued NSSI research and wanted to contribute to such work. Third, participants referenced feeling mentally and emotionally prepared to discuss their NSSI. The findings indicate that individuals who have not previously disclosed their NSSI may wish to discuss their experience in research for a variety of reasons. Such findings highlight implications for how we foster safe spaces in research for people with lived experience of NSSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".