Predictors of Place of Death for Seniors in Ontario: A Population-Based Cohort Analysis
Bibliographic record
Abstract
Place of death was determined for all 58,689 seniors (age > or = 66 years) in Ontario who died during fiscal year 2001/2002. The relationship of place of death to medical and socio-demographic characteristics was examined using a multinomial logit model. Half (49.2 %) of these individuals died in hospital, 30.5 per cent died in a long-term care facility, 9.6 per cent died at home while receiving home care, and 10.7 per cent died at home without home care. Co-morbidities were the strongest predictors of place of death (p < 0.0001). A cancer diagnosis increased the chances of death at home while receiving home care; seniors with dementia were most likely to die in LTC facilities; and those with major acute conditions were most likely to die in hospitals. Higher socio-economic status was associated with greater probability of dying at home but contributed little to the model. Appropriate planning and resource allocation may help move place of death from hospitals to nursing homes or the community, in accordance with individual preferences.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".