A Systematic Review of Reviews of Correctional Mental Health Services Using the STAIR Framework
Bibliographic record
Abstract
BACKGROUND: Rising demand for correctional mental health services (CMHS) in recent decades has been a global phenomenon. Despite increasing research, there are major gaps in understanding the best models for CMHS and how to measure their effectiveness, particularly studies that consider the overall care pathways and effectiveness of service responses. The STAIR (Screening, Triage, Assessment, Intervention, and Re-integration) model is an evidence-based framework that defines and measures CMHS as a clinical pathway with a series of measurable, and linked functions. METHOD: We conducted a systematic review of the reviews of CMHS elements employing PRISMA guidelines, organized according to STAIR pillars. We assessed the quality of included studies using the AMSTAR-2 criteria. Narrative reviews were read and results synthesized. RESULTS: We included 26 review articles of which 12 were systematic, metaanalyses, and 14 narrative reviews. Two systematic reviews and seven narrative reviews addressed screening and triage with strong evidence to support specific screening and triage systems. There was no evidence for standardised assessment approaches. Eight systematic reviews and seven narrative reviews addressed interventions providing some evidence to support specific psychosocial interventions. Three systematic reviews and six narrative reviews addressed reintegration themes finding relatively weak evidence to support reintegration methods, with interventions often being jurisdictionally specific and lacking generalizability. CONCLUSIONS: The STAIR framework is a useful way to organize the extant literature. More research is needed on interventions, assessment systems, care pathway evaluations, and reintegration models.
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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 | 0.037 | 0.138 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.036 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".