Drug Toxicity Deaths after Release from Incarceration in Ontario, 2006-2013: Review of Coroner’s Cases
Bibliographic record
Abstract
BACKGROUND: There is an increased risk of death due to drug toxicity after release from incarceration. The purpose of this study was to describe the timing, rate and circumstances of drug toxicity deaths following release from incarceration. This information can be used to help design potential preventive interventions. METHODS AND FINDINGS: We reviewed coroner's files to identify deaths in adults in Ontario between 2006 and 2013 caused by drug toxicity (n = 6,978) and these records were matched with provincial correctional records to identify individuals who died within one year of being released from incarceration (n = 702). Twenty percent (n = 137) of the 702 deaths occurred within one week of release. The majority (77%, n = 538) of deaths after release involved one or more opioids. Of the deaths involving opioids, intervention by another person may have been possible in 318 cases. CONCLUSIONS: Between 2006 and 2013 in Ontario, one in ten drug toxicity deaths in adults occurred within one year of release from provincial incarceration. These findings may help to inform the implemention and assessment of interventions aimed at reducing drug toxicity deaths following release from incarceration.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".