Violence-Related Deaths Among People Released From Prison: A Data Linkage Study
Bibliographic record
Abstract
People released from prison are a socially marginalized group and are at high risk of death from preventable causes, including violence. Despite this, little is known about the epidemiology of violence-related death (VRD) after release from prison. This knowledge is essential for developing targeted, evidence-informed violence prevention strategies. We examined VRDs among a representative sample of people released from prisons in Queensland, Australia, by sex and Indigenous status. Correctional records for all people (aged ≥17 years) released from prisons from January 1994 until December 2007 ( N = 41,970) were linked probabilistically with the National Death Index. The primary outcome was VRD following release from prison. We calculated crude mortality rates (CMRs) and standardized mortality ratios (SMRs) standardized by age and sex to the Australian population. We used Cox regression to identify predictors of VRD. Of 2,158 deaths after release from prison, 3% ( n = 68) were violence-related. The SMR for VRD was 10.0 (95% confidence interval (CI): [7.9, 12.7]) and was greatest for women (SMR = 16.3, 95% CI: [8.2, 32.7]). The rate of VRD was 2.5 deaths per 10,000 person-years (95% CI: [2.0, 3.2]) and was highest between 2 and 6 months after release from prison (CMR = 6.3, 95% CI: [3.4, 11.6]). Risk factors for VRD included short sentences (<90 days; for males and non-Indigenous people) and experiencing two or more imprisonments (for non-Indigenous people). No significant risk factors for VRD were identified for women or Indigenous people. People released from prison die from violence at a rate that is greatly elevated compared with the general population, with women experiencing the greatest elevation in risk. Reducing the number of VRDs in this population could improve the health and wellbeing of some of our most marginalized community members.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".