Factors predicting coroners’ decisions to hold discretionary inquests
Bibliographic record
Abstract
BACKGROUND: Coroners in Australia, Canada, New Zealand and other countries in the Commonwealth hold inquests into deaths in two situations. Mandatory inquests are held when statutory rules dictate they must be; discretionary inquests are held based on the decisions of individual coroners. Little is known as to how and why coroners select particular deaths for discretionary inquests. METHODS: We analyzed the deaths investigated by Australian coroners for a period of seven and one-half years in five jurisdictions. We classified inquests as mandatory or discretionary. After excluding mandatory inquests, we used logistic regression analysis to identify the factors associated with coroners' decisions to hold discretionary inquests. RESULTS: Of 20 379 reported deaths due to external causes, 1252 (6.1%) proceeded to inquest. Of these inquests, 490 (39.1%) were mandatory and 696 (55.6%) were discretionary. In unadjusted analyses, the rates of discretionary inquests varied widely in terms of age of the decedent and cause of death. In adjusted analyses, the odds of discretionary inquests declined with the age of the decedent; the odds were highest for children (odds ratio [OR] 2.17, 95% confidence interval [CI] 1.54-3.06) and lowest for people aged 65 years and older (OR 0.38, 95% CI 0.28-0.51). Using poisoning as a reference cause of death, the odds of discretionary inquests were highest for fatal complications of medical care (OR 12.83, 95% CI 8.65-19.04) and lowest for suicides (OR 0.44, 95% CI 0.30-0.65). INTERPRETATION: Deaths that coroners choose to take to inquest differ systematically from those they do not. Although this vetting process is invisible, it may influence the public's understanding of safety risks, fatal injury and death.
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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 | 0.009 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".