Correspondence between self-report and interview-based assessments of antisocial personality disorder.
Bibliographic record
Abstract
Antisocial personality disorder (ASPD) is associated with suicide, violence, and risk-taking behavior and can slow response to first-line treatment for Axis I disorders. ASPD may be assessed infrequently because few efficient diagnostic tools are available. This study evaluated 2 promising self-report measures for assessing ASPD--the ASPD scale of the Personality Diagnostic Questionnaire-4 (PDQ-4; S. E. Hyler, 1994) and the Personality Assessment Inventory (PAI; L. Morey, 1991, 2007)--as well as the ASPD module of the Structured Clinical Interview for DSM-IV Axis II (SCID-II; M. B. First, R. L. Spitzer, M. Gibbon, J. B. W. Williams, & L. S. Benjamin, 1997). The measures were administered to 1,345 offenders in court-mandated residential substance abuse treatment programs and prisons. PDQ-4 and PAI scores related strongly to SCID-II symptom counts (rs = .67 and .51, respectively), indicating these measures convey useful clinical information about the severity of offenders' ASPD pathology. The dimensional association between the measures was relatively invariant across gender, race, and site, although differences in mean scores were observed. Levels of agreement of the SCID-II with the PDQ-4 (kappa = .31) and PAI (kappa = .32) in classifying participants as ASPD was limited. Alternative thresholds for both self-report measures were identified and cross-validated.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".