The Location of Death and Dying Across Canada: A Study Illustrating the Socio-Political Context of Death and Dying
Bibliographic record
Abstract
Background: Concern has existed for many years about the extensive use of hospitals by dying persons. In recent years, however, a potential shift out of hospital has been noticed in a number of developed countries, including Canada. In Canada, where high hospital occupancy rates and corresponding long waits and waitlists for hospital care are major socio-political issues, it is important to know if this shift has continued or if hospitalized death and dying remains predominant across Canada. Methods: Recent individual-anonymous population-level inpatient Canadian hospital data were analyzed to answer two questions: (1) what proportion of deaths in provinces and territories across Canada are occurring in hospital now? and (2) who is dying in hospital now? Results: In 2014–2015, 43.9% of all deaths in Canada (excluding Quebec) occurred in hospital. However, considerable cross-Canada differences in end-of-life hospital utilization were found. Some cross-Canada differences in hospital decedents were also noted, although most were older, male, and they died during a relatively short hospital stay after being admitted from their homes and through the emergency department after arriving by ambulance. Conclusion: Over half of all deaths in Canada are occurring outside of hospital now. Cross-Canada hospital utilization and inpatient decedent differences highlight opportunities for enhanced end-of-life care service planning and policy advancements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".