Examining the Methods Adolescents Use in Nonsuicidal Self‐Injury: A Multi‐Wave Latent Profile Analysis
Bibliographic record
Abstract
INTRODUCTION: Nonsuicidal self-injury (NSSI) among adolescents is a health concern. Little is known about the patterns of methods adolescents use, in terms of how many and how often different methods are used. METHODS: We used three annual waves of data from 630 Swedish adolescents (T1: age 12-18 years; 56% girls), who reported NSSI use at least once. Latent profile analysis was used to examine profile differences, with supplementary analyses focused on differences and change predicted by gender, internalizing, emotion dysregulation, interpersonal stressors, and severity of NSSI. RESULTS: Three profiles consistently emerged over time: one very low in NSSI, another with higher frequencies of cutting/scraping skin, and one reporting multiple methods of NSSI, ranging from moderate (T1) to high (T3) frequency. Profile subgroups differed: low subgroups consisted of the fewest girls and reported the lowest levels of intra- and interpersonal issues. Additionally, subgroups differed in severity of NSSI, suggesting damage to the skin may not be the only reason medical attention is needed. Significant change in subgroup membership was also observed. CONCLUSIONS: Although most adolescents engaged in NSSI at very low rates, many used multiple forms, differing in both frequency and versatility. Few differences were found between subgroups characterized by higher frequencies, suggesting that it might be possible to identify adolescents most in need of treatment by attending to the methods most frequently used. Results also suggested that measuring the severity of each method may yield more accurate information than a priori groupings.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".