Psychosocial approaches for individuals with schizophrenia in correctional and forensic psychiatric settings: a rapid review
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the psychosocial approaches that have been studied and for which positive outcomes have been reported for individuals with schizophrenia in correctional and forensic psychiatric settings. Design/methodology/approach A rapid review of the literature was undertaken. A search was conducted on MEDLINE and PsycNET electronic databases. Each identified approach was analyzed to define their types and components. Findings In total, 24 studies pertaining to 18 different psychosocial approaches were identified. Half of the studies used a quasi-experimental design with control group. Most frequent outcomes reported were improvements in knowledge about illness and problem solving. Seven studies reported positive outcomes related to issues more specific to this population (violence, aggression, and recidivism). Approaches associated with these studies used mainly traditional cognitive behavior therapy and cognitive remediation. The focus was on neurocognition, social cognition, social skills, emotion management and problem solving. Practical implications This rapid review may enlighten clinical settings on psychosocial approaches for which positive outcomes have been reported with individuals with schizophrenia in correctional and forensic psychiatric settings. The picture obtained supports the idea of using integrated rehabilitation approaches that cover the aforementioned intervention focuses with this population. Originality/value A significant contribution of the rapid review is based on the analysis of the psychosocial approaches identified. This process offers a closer look at the nature and content of the approaches used according to the outcomes reported.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".