Psychological dimensions of antisocial personality disorder as predictors of psychotherapy noncompletion among sexual offenders
Bibliographic record
Abstract
The goal of this study was to examine whether psychological dimensions of antisocial personality disorder (ASPD), as conceptualized by Kernberg (1992), could predict psychotherapy noncompletion (PNC) among 50 men found guilty of sexual abuse of children. All participants began a 65-week, court-mandated course of cognitive-behavioral psychotherapy, which 20 (40%) of them did not complete. Pretherapy personality was assessed with the Structured Clinical Interview for DSM Axis II Disorders (First, Spitzer, Gibbon, Williams, & Benjamin, 1997), the Personality Organization Diagnostic Form (Diguer, Normandin, & Hébert, 2001), and Blatt and colleagues' (Blatt, Bers, & Schaffer, 1993; Blatt, Chevron, Quinlan, Schaffer, & Wein, 1988) scales of mental representations, as well as the State-Trait Anger Expression Inventory (Spielberger, 1988). A discriminant function analysis, which explained 46% of the total variance, showed that descriptive (antisocial and narcissistic personality disorders), psychological (primitive defense mechanisms, identity diffusion and self-representations), and demographic (work status and income) variables predicted PNC. The classification analysis correctly classified 78% of the participants. These findings support the hypothesis that psychological dimensions of ASPD help explain PNC among sexual offenders. The authors discuss the theoretical and clinical implications of these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".