Solitary Confinement of Inmates Associated With Relapse Into Any Recidivism Including Violent Crime: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Recidivism among released inmates is associated with a substantial societal burden given the financial and medical consequences of victimization. Among incarcerated North Americans, approximately 7% are housed in solitary confinement (SC). Studies show SC can lead to psychological deterioration and dispute it can effectively reduce institutional misconduct or recidivism. This meta-analysis aims to clarify the impact of SC on postrelease recidivism, which we hypothesized would increase following SC. A meta-analysis was conducted using PubMed, PsycINFO, Web of Science, and Google Scholar databases from inception until December 2019. Studies on adult inmates in correctional settings were included if they met an operational definition of SC, measured recidivism, and included a comparison group in general inmate population. Random-effect models were used to assess the impact of SC on multiple types of recidivism. Of the 2,713 identified records, 12 met inclusion criteria ( n = 194,078). A moderate association was found between SC and any recidivism (odds ratio [ OR] = 1.67, 95% confidence interval [1.41, 1.97]), which persisted in controlled studies ( OR = 1.41). This association was replicated across types of recidivism comprising violence ( OR = 1.41), rearrests ( OR = 1.37), and reincarceration ( OR = 1.67). Moreover, a more recent exposure to SC increased recidivism risk ( OR = 2.02), and a dose–response relationship was found between days in SC and recidivism. The overall database presented high heterogeneity but no publication bias. Findings show a small to moderate association between SC and future crime/violence. Considering the societal costs associated with antisocial behaviors following SC, mental health and psychosocial programming facilitating inmates’ successful reentry into society should be implemented and rigorously evaluated in strong research design.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".