The functions of nonsuicidal self-injury: converging evidence for a two-factor structure
Bibliographic record
Abstract
Research has identified more than a dozen functions of non-suicidal self-injury (NSI), but the conceptual and empirical overlap among these functions remains unclear. The present study examined the structure of NSI functions in two large samples of patients receiving acute-care treatment for NSI. Two different measures of NSI functions were utilized to maximize generalizability of findings: one sample (n = 946) was administered the Inventory of Statements About Self-injury (ISAS; Klonsky and Glenn in J Psychopathol Behav Assess 31:215-219, 2009), and a second sample (n = 211) was administered the Functional Assessment of Self-Mutilation (FASM; Lloyd et al. in Self-mutilation in a community sample of adolescents: descriptive characteristics and provisional prevalence rates. Poster session at the annual meeting of the Society for Behavioral Medicine, New Orleans, LA, 1997). Exploratory factor analyses revealed that both measures exhibited a robust two-factor structure: one factor represented Intrapersonal functions, such as affect regulation and anti-dissociation, and a second factor represented Social functions, such as interpersonal influence and peer bonding. In support of the two-factor structure's construct validity, the factors exhibited a pattern of correlations with indicators of NSI severity that was consistent with past research and theory. Findings have important implications for theory, research, and treatment. In particular, the two-factor framework should guide clinical assessment, as well as future research on the implications of NSI functions for course, prognosis, treatment, and suicide risk.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.000 |
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".