Exploring Unique Patterns of Self‐Injury Recovery: A Latent Profile Analysis
Bibliographic record
Abstract
BACKGROUND: As nonsuicidal self-injury (NSSI) has become an increasing public health concern, the last few years have seen the emergence of efforts to address NSSI recovery. Although many recovery efforts adopt a medical view of self-injury and focus on cessation of the behaviour, recovery can mean many different things to different people. In this study, we provide initial empirical validation of the self-injury recovery framework, by assessing whether different recovery profiles exist. METHODS: Our sample comprised 733 participants with lived experience of NSSI (M age = 24.54, sd = 6.39). Participants completed self-report measures of constructs related to NSSI recovery and NSSI characteristics. RESULTS: Using latent profile analysis, we identified six unique profiles reflecting differences in thoughts/urges to self-injure, self-efficacy, social support, optimism, coping, underlying adversities, perceptions of scarring, disclosure, resilience and self-compassion. Multivariate analyses of variance confirmed these profiles differed according to NSSI characteristics such as frequency of NSSI, a self-assessment of recovery, the desire to self-injure or avoid self-injury and the number of people disclosed to. LIMITATIONS: A homogenous sample and cross-sectional design limit generalisability of our findings across populations and across time. CONCLUSIONS: Our findings reinforce that recovery can take many different forms, with different factors being relevant to different individuals. Adopting a person-centred approach that centres an individual's lived experience and emphasises what is important to them in the recovery process offers opportunities for more empathic responses to self-injury and better outcomes for individuals 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".