Can Antisocial Personality Disorder Be Treated? A Meta-Analysis Examining the Effectiveness of Treatment in Reducing Recidivism for Individuals Diagnosed with ASPD
Bibliographic record
Abstract
The effectiveness of treatment for individuals diagnosed with Antisocial Personality Disorder (ASPD) has been questioned and debated for years. As individuals with ASPD are considerably overrepresented in the criminal justice system, the ability of treatment to reduce recidivism is a prominent concern. The present meta-analysis identified six unique controlled and uncontrolled treatment outcome studies investigating the effectiveness of treatment in reducing general/any recidivism for individuals with ASPD. Results from the controlled studies indicated no significant differences in recidivism rates between individuals with ASPD in treatment and those in treatment as usual; however, the direction of the odds ratios suggested lower recidivism for the treatment groups. Results from the uncontrolled studies suggested equal effectiveness of treatment when comparing individuals with and without ASPD; however, these effects may not be attributable to the treatment in question. Interpretation of these findings and the generalizability of the general offender treatment literature to individuals with ASPD is discussed.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".