Associations Between Supported Accommodation and Health and Re-offending Outcomes: a Retrospective Data Linkage Study
Bibliographic record
Abstract
Following release from prison, housing and health issues form a complex and mutually reinforcing dynamic, increasing reincarceration risk. Supported accommodation aims to mitigate these post-release challenges. We describe the impact of attending Rainbow Lodge (RL), a post-release supported accommodation service for men in Sydney, Australia, on criminal justice and emergency health outcomes. Our retrospective cohort study using linked administrative data includes 415 individuals referred to RL between January 2015 and October 2020. Outcomes of interest were rates of criminal charges, emergency department (ED) presentations and ambulance attendance; and time to first reincarceration, criminal charge, ED presentation and ambulance attendance. The exposure of interest was attending RL; covariates included demographic characteristics, release year and prior criminal justice and emergency health contact. Those who attended RL (n = 170, 41%) more commonly identified as Aboriginal or Torres Strait Islander (52% vs 41%; p = 0.025). There was strong evidence that attending RL reduced the incidence criminal charges (adjusted rate ratio [ARR] = 0.56; 95% confidence interval [CI] 0.340.86; p = 0.009). Absolute rates indicate a weak protective effect of RL attendance on ED presentation and ambulance attendance; however, adjusted analyses indicated no evidence of an association between attending RL and rates of ED presentations (ARR = 0.88; 95% CI = 0.65-1.21), or ambulance attendance (ARR = 0.82; 95% CI = 0.57-1.18). There was no evidence of an association between attending RL and time to first reincarceration, charge, ED presentation or ambulance attendance. Greater detail about reasons for emergency health service contact and other self-report outcome measures may better inform how supported accommodation is meeting its intended aims.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".