The prevalence and correlates of dual diagnosis among adults in custody: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Incarcerated individuals experience mental illness (MI), substance use disorders (SUD), and their co-occurrence - dual diagnosis - at higher rates than the general population. By systematically reviewing the literature on dual diagnosis in custody, we aimed to (1) estimate the pooled prevalence of dual diagnosis among adults in custody, and (2) identify the psychosocial, health-related, and criminal justice correlates of dual diagnosis. METHOD: We searched CINAHL, CINCH, Embase, Medline, PsycINFO, and Web of Science for studies investigating dual diagnosis among adults in custody. We also conducted backward citation chaining of a previous systematic review of dual diagnosis in Australian prisons. We used random-effects meta-analysis to generate a pooled prevalence estimate of dual diagnosis and conducted a narrative synthesis of the identified correlates of dual diagnosis in the literature. RESULTS: Twenty-five studies met the inclusion criteria; 20 had sufficient data for meta-analysis. The pooled prevalence estimate of dual diagnosis among adults in custody was 25.3 % [95 %CI: 18.6, 32.7]. Correlates of dual diagnosis included illicit substance use before 15 years old, living with someone who used substances before incarceration, violence victimisation, increased suicide risk, and a lifetime history of multiple convictions. CONCLUSIONS: Our findings suggest that approximately one out of every four adults in custody have a dual diagnosis, highlighting the need for coordinated mental health and alcohol and other drug services for justice-involved individuals. It is crucial that correctional healthcare providers have the capacity and resources necessary to address the complex needs of adults with dual diagnosis in custody.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".