Do Psychopathic Traits Assessed in Mid-Adolescence Predict Mental Health, Psychosocial, and Antisocial, Including Criminal Outcomes, over the Subsequent 5 Years?
Bibliographic record
Abstract
OBJECTIVE: To determine whether psychopathic traits assessed in mid-adolescence predicted mental health, psychosocial, and antisocial (including criminal) outcomes 5 years later and would thereby provide advantages over diagnosing conduct disorder (CD). METHOD: Eighty-six women and 61 men were assessed in mid-adolescence when they first contacted a clinic for substance misuse and were reassessed 5 years later. Assessments in adolescence include the Psychopathy Checklist-Youth Version (PCL-YV), and depending on their age, either the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Aged Children or the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (SCID). Assessments in early adulthood included the SCID, self-reports of psychosocial functioning, aggressive behaviour, and criminality and official criminal records. RESULTS: The antisocial facet score positively predicted the number of anxiety symptoms and likelihood of receiving treatment for substance use disorders (SUDs). Lifestyle and antisocial facet scores negatively predicted Global Assessment of Functioning scores. By contrast, the interpersonal score and male sex independently and positively predicted the number of months worked or studied, as did the interaction of Lifestyle × Sex indicating that among men, but not women, an increase in lifestyle facet score was associated with less time worked or studied. Interpersonal and antisocial scores positively predicted school drop-out. Antisocial facet scores predicted the number of symptoms of antisocial personality disorder, alcohol and SUDs, and violent and nonviolent criminality but much more strongly among males than females. Predictions from numbers of CD symptoms were similar. CONCLUSIONS: Psychopathic traits among adolescents who misuse substances predict an array of outcomes over the subsequent 5 years. Information on the levels of these traits may be useful for planning treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".