Days Spent at Home before Death from Cancer for Immigrants and Long-Term Residents in Ontario, Canada
Bibliographic record
Abstract
Background: Time at home before death is an emerging patient-centered metric of quality end-of-life care. It is unknown if immigrants who die from cancer in Ontario spend less time at home near the end of life. Objective: Compare the number of days at home (DAH) in the last six months of life for immigrants and long-term residents (LTRs) who die from cancer. Methods: Population-based cohort study (January 1, 2005 to December 31, 2013) using administrative databases. Participants were adults (≥18 years) who died from cancer in Ontario. Immigrants were defined as those who immigrated from 1985 onward. The outcome was DAH in the last six months of life. Analysis included univariate and multivariable regression, adjusting for patient and disease characteristics. Subgroup analyses assessed DAH by immigration class, time since immigration, and region of birth. Sensitivity analyses excluded patients with breast and prostate cancer to examine for sex differences. Results: Seventy-two thousand nine hundred eighty-eight individuals (3988 immigrants) were identified. Immigrants spent fewer DAH in the last six months (unadjusted 162 days vs. 164 days, p < 0.001). This remained statistically significant after adjusting (p = 0.0087). DAH varied by immigration class and region of birth. Sensitivity analyses suggest a sex difference in end-of-life time spent at home. Conclusions: Immigrants who die from cancer in Ontario spend fewer DAH before death than LTRs. This may be due to patient preferences, inequitable access to services, or availability of local relatives for support. Further research is needed to understand the causes of this association.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".