Does access to end-of-life homecare nursing differ by province and community size?: A population-based cohort study of cancer decedents across Canada
Bibliographic record
Abstract
BACKGROUND: Studies have demonstrated the strong association between increased end-of-life homecare nursing use and reduced acute care utilization. However, little research has described the utilization patterns of end-of-life homecare nursing and how this differs by region and community size. METHODS: A retrospective population-based cohort study of cancer decedents from Ontario, British Columbia, and Nova Scotia was conducted between 2004 and 2009. Provinces linked administrative databases which provide data about homecare nursing use for the last 6 months of life for each cancer decedent. Among weekly users of homecare nursing in their last six months of life, we describe the proportion of patients receiving end-of-life homecare nursing by province and community size. RESULTS: Our cohort included 83,746 cancer decedents across 3 provinces. Patients receiving end-of-life nursing among homecare nursing users increased from weeks -26 to -1 before death by: 78% to 93% in British Columbia, 40% to 81% in Ontario, and 52% to 91% in Nova Scotia. In all 3 provinces, the smallest community size had the lowest proportion of patients using end-of-life nursing compared to the second largest community size, which had the highest proportion. CONCLUSIONS: Differences in end-of-life homecare nursing use are much larger between provinces than between community sizes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".