Determinants of place of death: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: As Canada's population ages, the location of end of life care (whether at home, extended care facility or hospital) may change depending on the location of death. We carried out a study to identify determinants of the place of death. METHODS: Data on deaths in British Columbia between 2004 and 2008 were obtained from the Vital Statistics Agency. Place of death was categorized into home, extended care facility, hospital or other. Logistic regression analyses were used to estimate the effects of age, sex, marital status, residence, place of birth and cause of death on place of death using adjusted odds ratios and 95% confidence intervals (95% CI). RESULTS: Of the 153,111 deaths in the study, 16.5% occurred at home, 29.0% in extended care, 51.0% in hospital and 3.5% occurred elsewhere. Male deaths were less likely to occur in extended care as compared with female deaths (odds ratio 0.73, 95% CI 0.71-0.75). Age (odds ratio 3.31, 95% CI 3.19-3.45 for those for ≥90 vs 70-79 years), marital status (odds ratio 1.42, 95% CI 1.38-1.47 widowed vs married), residence (odds ratio 0.80, 95% CI 0.76-0.83 rural vs Vancouver), place of birth (odds ratio 0.80, 95% CI 0.75-0.86 China vs Canada) and cause of death (odds ratio 3.91, 95% CI 3.69-4.13 dementia vs cancer) were also associated with death in extended care. CONCLUSIONS: Information on determinants of place of death can inform public health policy regarding care at the end of life and make resource allocation more efficient.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.001 | 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".