Examining the Contemporary Use of Hospitals in Canada for Palliative Care Purposes: A Population-Based Study to Enable Policy and Program Developments
Bibliographic record
Abstract
Background:It is commonly thought that most deaths in developed countries take place in hospital. Death place is a palliative care quality indicator. Objectives:To determine the use of Canadian hospitals by patients who died in hospital during the 2019–2020 year and any additional hospital utilization occurring over their last 365 days of life. Measurements:Describe patients who died in hospital, and any additional use of hospitals by these patients over their last year of life. Results:Ninety-one thousand six hundred forty inpatients died during 2019–2020; 4.85% of all 1.88 million hospitalized individuals and 41.82% of all deaths in Canada that year. Decedents were primarily 65+ years of age (81.16%), male (53.44%), admitted through an emergency department (80.16%), and arrived by ambulance (72.15%). The most common diagnosis was the nonspecific ICD-10 defined “factors influencing health status and contact with health services” (23.75%), followed by “circulatory diseases” (18.22%), “respiratory diseases” (15.58%), and many other less common diagnoses. The average length of final hospital stay was 16.54 days, with 89.97% having some Alternative Level of Care (ALC) or ALC days recorded, indicating another care setting was preferable. Only 5.78% had cardiopulmonary resuscitation performed during their final hospitalization. Of all 91,640 decedents, 74.33% had only one admission to hospital in their last 365 days of life, while 25.67% (more often younger than older decedents) had two to five admissions. Conclusions:This study confirms a continuing shift of death and dying out of hospital in Canada. Most deaths and end-of-life care preceding death take place outside of hospitals now. Enhanced community-based services are recommended to support optimal dying processes outside of hospitals and also help more dying people avoid hospital deaths.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".