DSM-5 antisocial personality disorder: Predictive validity in a prison sample.
Bibliographic record
Abstract
Symptoms of antisocial personality disorder (ASPD), particularly remorselessness, are frequently introduced in legal settings as a risk factor for future violence in prison, despite a paucity of research on the predictive validity of this disorder. We examined whether an ASPD diagnosis or symptom-criteria counts could prospectively predict any form of institutional misconduct, as well as aggressive and violent infractions among newly admitted prisoners. Adult male (n = 298) and female (n = 55) offenders were recruited from 4 prison systems across the United States. At the time of study enrollment, diagnostic information was collected using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997) supplemented by a detailed review of official records. Disciplinary records were obtained from inmates' respective prisons covering a 1-year period following study enrollment and misconduct was categorized hierarchically as any (general), aggressive (verbal/physical), or violent (physical). Dichotomous ASPD diagnoses and adult symptom-criteria counts did not significantly predict institutional misconduct across our 3 outcome variables, with effect sizes being close to 0 in magnitude. The symptom of remorselessness in particular showed no relation to future misconduct in prison. Childhood symptom counts of conduct disorder demonstrated modest predictive utility. Our results offer essentially no support for the claim that ASPD diagnoses can predict institutional misconduct in prison, regardless of the number of adult symptoms present. In forensic contexts, testimony that an ASPD diagnosis identifies defendants who will pose a serious threat while incarcerated in prison presently lacks any substantial scientific foundation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".