Lived Experience Perspectives on Self-Injury
Bibliographic record
Abstract
Abstract Although there have been many important gains in our understanding of self-injury over the past couple of decades, the majority of this research has emerged from the perspective of clinicians and researchers, overlooking the important insights provided by individuals who have lived experience of nonsuicidal self-injury (NSSI). To date, research has typically asked participants about their experiences rather than involving them as active participants in the research. While this has yielded a better understanding of NSSI, recovery, disclosure, and stigma, giving individuals with lived NSSI experience a more active role in research may inform what and how we study NSSI and may also benefit participants themselves (e.g., by fostering greater insight or feelings of contribution). There is a moral imperative to involve individuals with lived experience at all stages of research, as well as in clinical care and advocacy settings. In doing so, this chapter acknowledges the ethical challenges that may emerge, primarily protection of vulnerable persons and peoples and inclusion/representation of diverse experiences. These challenges, however, should not be a barrier to centering lived experience perspectives but, rather, should be acknowledged in order to improve the quality of NSSI research, treatment approaches, and advocacy initiatives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".