Is mental illness associated with placement into solitary confinement in correctional settings? A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract The aim of this meta‐analysis was to examine the association between any mental health problem and the risk of being placed into solitary confinement in correctional settings. PubMed, PsycINFO, Web of Science and Google Scholar were searched from each database’s inception date to November 2019. All publications assessing both mental health problems and placement into solitary confinement in a sample of adult inmates in correctional settings were included. The meta‐analysis was performed using random‐effects models. Heterogeneity among study point estimates was assessed with Q statistics and quantified with I2 index. Publication bias was assessed with funnel plots. Guidelines from Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) were followed throughout. After screening 2777 potential studies, 11 studies were included amounting to a total of 163 414 inmates. Included studies comprised of a mix of mental disorders rather than a specific diagnosis per se. The odds ratio (OR) from the pooled studies was 1.62 (confidence interval (CI) = 1.21–2.15). The observed relationship remained unchanged regardless of the removal of outliers (OR = 1.63, CI = 1.47–1.80) and regardless of the adjustment of confounders (OR = 1.58, CI = 1.32–1.88). The present study shows a moderate association between any mental health problem and placement into solitary confinement within a considerable sample of inmates. As more individuals suffering from mental illness enter the correctional system, it is essential that correction officials create new safe interventions to manage these inmates and offer them proper mental health care to limit the use of solitary confinement, which may have deleterious effects.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.006 | 0.007 |
| 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".