Dying in Canada: Is It an Institutionalized, Technologically Supported Experience?
Bibliographic record
Abstract
Although preliminary evidence shows that people generally prefer to die at home, very little is known about where Canadians die. Understanding the epidemiology of dying in Canada may illuminate opportunities to improve quality of end-of-life care and related health policy. We conducted a cross-sectional analysis of death records in Canada to determine the proportions of deaths occurring in hospitals and special care units. Our analysis found that deaths in Canada occur in hospitals with provincial and territorial proportions ranging from 87% in Quebec to 52% in the Northwest Territories. In hospitals recording deaths in special care units, 18.64% of all deaths occurred in special care units. The proportion of deaths in special care units ranged from 25% in Manitoba to 7% in the Northwest Territories. The proportion of deaths in special care units varied by size and nature (teaching vs. non-teaching) of hospitals. It increased with the size of the hospital from 8% in hospitals with 1-49 beds, to 23% for hospitals with 400 or more beds. In teaching hospitals, 27% of deaths occurred in special care units, and in non-teaching hospitals the proportion was 15%. In conclusion, the majority of deaths in Canada occur in hospitals and a substantial proportion occur in special care units, raising questions about the appropriateness and quality of current end-of-life care practices in Canada.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".