The impact of supported accommodation on health and criminal justice outcomes of people released from prison: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: Supported accommodation intends to address challenges arising following release from prison; however, impact of services, and of specific service components, is unclear. We describe key characteristics of supported accommodation, including program components and outcomes/impact; and distil best-evidence components. METHODS: We conducted a systematic review, searching relevant databases in November 2022. Data were synthesised via effect direction plots according to the Synthesis Without Meta-analysis guidelines. We assessed study quality using the McGill Mixed Methods Appraisal Tool, and certainty in evidence using the GRADE framework. RESULTS: Twenty-eight studies were included; predominantly cross-sectional. Program components which address life skills, vocational training, AOD use, and mental health appear to positively impact criminal justice outcomes. Criminal justice outcomes were the most commonly reported, and while we identified a reduction in parole revocations and reincarceration, outcomes were otherwise mixed. Variable design, often lacking rigour, and inconsistent outcome reporting limited assessment of these outcomes, and subsequently certainty in findings was low. CONCLUSION: Post-release supported accommodation may reduce parole revocations and reincarceration. Despite limitations in the literature, the findings presented herein represent current best evidence. Future studies should clearly define program components and measure their impact; use analyses which reflect the high risk of adverse outcomes, such as time-to-event analyses; and consider outcomes which reflect the range of challenges faced by people leaving prison. REGISTRATION: PROSPERO registration CRD42020189821.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".