Association between Mental Illness and Disciplinary Confinement and Its Effect on Mental Health: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The aims of this systematic review and meta-analysis were to evaluate the risk of inmates with mental disorder of being placed into disciplinary confinement (DC) and its effect on mental health. A systematic search of studies was performed in PubMed, PsycINFO, Web of Science, and Google Scholar. The meta-analysis was conducted using random-effects models. Heterogeneity among study point estimates was assessed with Q statistics and quantified with I2 index. Publication bias was assessed with Egger’s test. Quality assessment was based on the GRADE Checklist for observational studies. Guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were followed throughout. First, a meta-analysis of 5 articles including 27,455 inmates showed than incarcerated individuals with a mental disorder were 1.23 times (OR=1.23, CI=1.10; 1.38) more likely to be placed in DC than incarcerated individuals without a mental disorder. Particularly, having a severe mental disorder (OR=1.31, p<0.001), a personality disorder (OR=1.66, p<0.001) and having previously received mental health services (OR=1.16, p=0.024) increased the risk of being placed in DC. Secondly, a systematic review of 5 articles including 171,300 inmates showed more psychological distress, psychiatric symptoms (self-harm, thought disorders, obsessive-compulsive symptoms), need for mental health services and hospitalizations in DC than the general correctional population. Considering the increased risk of placement in DC for incarcerated persons with a mental disorder and its deleterious effect on mental state, it is essential that correction officials create new safe interventions to manage these inmates and offer them proper mental health care to limit its use.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".