Where a Cancer Patient Dies: The Effect of Rural Residency
Bibliographic record
Abstract
CONTEXT: Surveys indicate 50% to 80% of cancer patients would choose to die at home if possible, although far fewer actually do. In Nova Scotia (NS), cancer deaths occurring out-of-hospital increased from 19.8% in 1992 to 30.2% in 1997. The impact of rural residency on this trend has not been studied. PURPOSE: To determine the association between dying of cancer in a rural locale and the likelihood of it being an out-of-hospital death. METHODS: Secondary analysis of linked population-based administrative health data files. Subjects were all Nova Scotians who died of cancer from 1992 to 1997. Measures included location of death, dichotomized as a hospital death or an out-of-hospital death; and urban-rural residency, using an enumeration area urban-rural indicator created from postal code information adjusted for individual characteristics. RESULTS: Of the 13,652 total cancer deaths, 6171 occurred in rural NS, of whichl 1471 (23.8%) died out-of-hospital. Out-of-hospital deaths in rural NS increased from 16.2% in 1992 to just over 27% in 1997. Compared with urban cancer patients, the adjusted odds of an out-of-hospital death in rural NS was lower (adjusted odds ratio, 0.87; 95% confidence interval, 0.79-0.95). CONCLUSIONS: There was an increasing trend during the 1990s for cancer patients to die out-of-hospital. Compared with their urban counterparts, patients in rural areas were less likely to do so. Those with cancer living in the rural setting who wish to die at home may face unique challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".