Descriptive Characteristics and Initial Psychometric Properties of the Non-Suicidal Self-Injury Disorder Scale
Bibliographic record
Abstract
Non-suicidal self-injury (NSSI) is highly prevalent and associated with tissue damage, emotional distress, and psychiatric disorders. While often discussed in the context of Borderline Personality Disorder and suicide, research demonstrates that NSSI is distinct from these constructs and should be viewed as an independent diagnostic category. Recently, Non-Suicidal Self-Injury Disorder (NSSID) was included in the revised Diagnostic and Statistical Manual of Mental Disorders as a condition for further study. In this article, we describe the properties of a self-report measure designed to assess proposed criteria for NSSID. Undergraduate students at 2 large, public universities completed the NSSID Scale (NSSIDS) along with other measures of NSSI characteristics and psychopathology. Among participants with a history of NSSI, approximately half (54.55%) met diagnostic criteria for NSSID. Participants were most frequently excluded from an NSSID diagnosis on the basis of criterion A (frequency of NSSI) and criterion E (distress or impairment related to NSSI), while participants were least likely to be excluded from diagnosis on the basis of criterion D (NSSI method exclusions) and criterion F (diagnostic "rule-outs"). Consistent with previous literature, the most commonly reported precipitants to NSSI were negative feelings or thoughts (criterion C2). Participants who met criteria for NSSID reported more severe depression, anxiety, and NSSI than participants who engaged in NSSI but did not meet criteria for NSSID. These results support the use of the NSSIDS as a reliable and valid self-report measure of NSSID symptoms.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".