Benchmarking time to initiation of end-of-life homecare nursing: a population-based cancer cohort study in regions across Canada
Bibliographic record
Abstract
BACKGROUND: Several studies have demonstrated the benefits of early initiation of end-of-life care, particularly homecare nursing services. However, there is little research on variations in the timing of when end-of-life homecare nursing is initiated and no established benchmarks. METHODS: This is a retrospective cohort study of patients with a cancer-confirmed cause of death between 2004 and 2009, from three Canadian provinces (British Columbia, Nova Scotia, and Ontario). We linked multiple administrative health databases within each province to examine homecare use in the last 6 months of life. Our primary outcome was mean time (in days) to first end-of-life homecare nursing visit, starting from 6 months before death, by region. We developed an empiric benchmark for this outcome using a funnel plot, controlling for region size. RESULTS: Of the 28 regions, large variations in the outcome were observed, with the longest mean time (97 days) being two-fold longer than the shortest (55 days). On average, British Columbia and Nova Scotia had the first and second shortest mean times, respectively. The province of Ontario consistently had longer mean times. The empiric benchmark mean based on best-performing regions was 57 mean days. CONCLUSIONS: Significant variation exists for the time to initiation of end-of-life homecare nursing across regions. Understanding regional variation and developing an empiric benchmark for homecare nursing can support health system planners to set achievable targets for earlier initiation of end-of-life care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".