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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".