Association between Chinese or South Asian ethnicity and end-of-life care in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Ethnicity may be associated with important aspects of end-of-life care, such as what treatments are received, access to palliative care and where people die. However, most studies have focused on end-of-life care of white, Hispanic and black patients. We sought to compare end-of-life care delivered to people of Chinese and South Asian ethnicity with that delivered to others from the general population, in Ontario, Canada. METHODS: In this population-based cohort study, we included all people who died in Ontario, Canada, between Apr. 1, 2004, and Mar. 31, 2015. People were identified as having Chinese or South Asian ethnicity on the basis of a validated surname algorithm. We used modified Poisson regression analyses to assess location of death and care received in the last 6 months of life. RESULTS: We analyzed 967 339 decedents, including 18 959 (2.0%) of Chinese and 11 406 (1.2%) of South Asian ethnicity. Chinese (13.6%) and South Asian (18.5%) decedents were more likely than decedents from the general population (10.1%) to die in the intensive care unit (ICU). The adjusted relative risk of dying in intensive care was 1.21 (95% confidence interval [CI] 1.15 to 1.27) for Chinese and 1.25 (95% CI 1.20 to 1.30) for South Asian decedents. In their last 6 months of life, decedents of Chinese and South Asian ethnicity experienced significantly more ICU admission, hospital admission, mechanical ventilation, dialysis, percutaneous feeding tube placement, tracheostomy and cardiopulmonary resuscitation than the general population. INTERPRETATION: Decedents of Chinese and South Asian ethnicity in Ontario were more likely than decedents from the general population to receive aggressive care and to die in an ICU. These findings may be due to communication difficulties between patients and clinicians, differences in preferences about end-of-life care or differences in access to palliative care services.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".