Diagnosis and Characterization of <i>DSM-5</i> Nonsuicidal Self-Injury Disorder Using the Clinician-Administered Nonsuicidal Self-Injury Disorder Index
Bibliographic record
Abstract
Despite the inclusion of nonsuicidal self-injury disorder (NSSID) in the DSM-5, research on NSSID is limited and no studies have examined the full set of DSM-5 NSSID diagnostic criteria. Thus, this study examined the reliability and validity of a new structured diagnostic interview for NSSID (the Clinician-Administered NSSI Disorder Index; CANDI) and provides information on the clinical characteristics and features of DSM-5 NSSID. Data on the interrater reliability, internal consistency, and construct validity of the CANDI and associated characteristics of NSSID were collected in a community sample of young adults (N = 107) with recent recurrent NSSI (≥10 lifetime episodes of NSSI, at least one episode in the past year). Participants completed self-report measures of NSSI characteristics, psychopathology, and emotion dysregulation, as well as diagnostic interviews of borderline personality disorder (BPD) and lifetime mood, anxiety, and substance use disorders. The CANDI demonstrated good interrater reliability and adequate internal consistency. Thirty-seven percent of participants met criteria for NSSID. NSSID was associated with greater clinical and diagnostic severity, including greater NSSI versatility, greater emotion dysregulation and psychopathology, and higher rates of BPD, bipolar disorder, posttraumatic stress disorder, social anxiety disorder, and alcohol dependence. Findings provide support for the reliability, validity, and feasibility of the CANDI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".