In-Region Versus Out-of-Region Hospitalizations at the End of Life Among Older Rural Residents: The Relationship Between Personal and System-Related Factors
Bibliographic record
Abstract
BACKGROUND: Even though a large segment of the population lives in rural areas, relatively little attention has been paid in the literature to date to hospital use at the end of life among rural residents. The objective of this study was to examine factors associated with in- or out-of-region hospitalizations at the end of life among older rural residents. METHODS: The study included all community-dwelling adults aged 65 or older living in rural regions of a mid-Western Canadian province who had died in fiscal years 2003-04 to 2005-06, as determined from Vital Statistics data (N = 5,550). Complete hospital discharge abstract data were used to identify in- or out-of-region hospitalizations in the last 6 months before death and on the day of death. The type of out-of-region hospitals older adults were admitted to was also examined (urban tertiary hospital, urban community hospital, and rural hospital). RESULTS: Twenty percent of hospitalizations and 21% of hospital deaths occurred in a hospital that was out of older adults' region of residence. Compared with decedents aged 65-74, those aged 75-84 and even more so those aged 85+ had reduced odds of being hospitalized out of region or dying in an out-of-region hospital. Those 85+ years old also had reduced odds of being hospitalized in a (out-of-region) tertiary hospital. Higher hospital bed rates and physician rates were associated with reduced odds of out-of-region hospitalization and hospital death. CONCLUSION: Efforts should focus on recruiting physicians to those rural areas with low physician rates, as well as finding mechanisms to retain physicians in those rural regions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".