Canadian Rural-urban Differences in End-of-life Care Setting Transitions
Bibliographic record
Abstract
Few studies have focused on the care setting transitions that occur in the last year of life. People living in rural areas may have more difficult care setting transitions and also more moves in the last year of life as health changes occur. A mixed-methods study was conducted to gain an understanding of the number and implications or impact of care setting transitions in the last year of life for rural Canadians. Rural Albertans had significantly more healthcare setting transitions than urbanites in the last year of life (M=4.2 vs 3.3). Online family respondents reported 8 moves on average occurred in the last year of life. These moves were most often identified (65%) on a likert-type scale as "very difficult," with the free text information revealing these trips were often emotionally painful for themselves and physically painful for the decedent. Eleven informants were then interviewed until data saturation, with constant-comparative data analysis conducted. Moving from place to place for needed care in the last year of life was identified as common and concerning for rural people and their families, with three data themes developing: (a) needed care in the last year of life is scattered across many places, (b) traveling is very difficult for terminally-ill persons and their caregivers, and (c) local rural services are minimal. These findings indicate planning is needed to avoid unnecessary end-of-life care setting transitions and to make needed moves for essential services in the last year of life less costly, stressful, and socially disruptive for rural people and their families.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".