The association between rurality, places of care and the location of death of long-term care home residents with dementia: A population-based study
Bibliographic record
Abstract
Background: Most individuals prefer to spend their final moments of life outside a hospital setting. This study compares the places of care and death of long-term care (LTC) home residents in Ontario in the last 90 days of life, according to LTC home rurality. Data and methods: This retrospective cohort study was conducted using health administrative data from ICES (formerly known as the Institute for Clinical Evaluative Sciences). The study population, which was identified through algorithms, included all Ontario LTC home residents with a dementia diagnosis who died between April 1, 2014, and March 31, 2019. The location of death was categorized as in an acute care hospital, an LTC home, a subacute care facility, or the community. Places of care included emergency department visits and hospitalizations in the last 90 days of life. Statistical tests were used to evaluate differences in location of death and places of care by rurality. Results: Of the 65,375 LTC home residents with dementia, 49,432 (75.6%) died in an LTC home. Residents of LTC homes in the most urban areas were less likely to die in an LTC home than those in more rural homes (adjusted relative risk: 0.84; 95% confidence interval: 0.83 to 0.85). A higher proportion of residents of the most urban LTC homes had at least one hospitalization in the last 90 days of life compared with rural residents (23.7% versus 9.9% palliative hospitalizations and 28.3% versus 15.9% non-palliative hospitalizations [p ⟨ 0.001]). Interpretation: Individuals with dementia residing in urban LTC homes are more likely to receive care in the hospital and to die outside a LTC home than their counterparts living in rural LTC homes. The findings of this work will inform efforts to improve end-of-life care for older adults with dementia living in LTC homes.
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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 | 0.002 |
| 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".