Executive Dysfunction in Criminal Populations: Comparing Forensic Psychiatric Patients and Correctional Offenders
Bibliographic record
Abstract
Background: Robust executive function (EF) deficits have been found in criminal groups and have been implicated as contributors to criminal behavior. A widely cited model of EF is made up of inhibition, shifting, and working memory. The current study compares these three EF components of two different criminal groups to one another and to a normative sample. Methods: EF of 42 forensic psychiatric patients was assessed and compared with 77 correctional offenders. EF was determined using the Delis-Kaplan Executive Function System (D-KEFS). Results: Forensic psychiatric patients display poorer performance on EF compared to correctional offenders. Overall, forensic psychiatric patients perform most poorly on measures of shifting. Furthermore, a large proportion of both forensic psychiatric patients (9.5–35.7%) and correctional offenders (5.2–27.3%) display clinically significant deficits in all components of EF compared to what would be expected in the normative population (2.5%). Conclusions/Implications: This study provides evidence of heterogeneity of cognitive deficits among different criminal populations and pervasive EF deficits in forensic and correctional populations compared to a normative sample. Understanding the unique EF profiles of different criminal groups can better inform rehabilitation programs and risk and release decisions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".