The socio-demographic determinants of place of death for seniors in Ontario
Bibliographic record
Abstract
This study determined the place of death (hospital, long-term care facility or home) for seniors in Ontario who died in Fiscal Year 2001 (n = 59,871), and examined the relationship between socio-demographic factors—age, gender, comorbidities in the last year of life, county of residence, long-term care (LTC) and hospital bed availability in county of residence, socio-economic status (SES), ethnicity, and immigrant status—and place of death. 29,479 (49.24%) individuals died in hospital, 13,316 (22.24%) died in LTC facilities, and 17,076 (28.52%) died at home. Comorbidities in the last year of life, particularly psychosocial and major acute conditions, were the strongest predictors of place of death (p < 0.0001), followed by age (p < 0.0001) and gender (p < 0.0001). Older individuals and women were less likely to die at home and more likely to die in LTC facilities. LTC and hospital bed av = availability, SES, ethnicity, and immigrant status were statistically significant predictors of place of death, but made a relatively small contribution to the final model. Our findings may guide policy and resource allocation decisions regarding palliative care in different settings. Research into other determinants of place of death and the costs and outcomes of palliative care in different care settings is recommended.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".