End-of-Life Cancer Care: Temporal Association between Homecare Nursing and Hospitalizations
Bibliographic record
Abstract
OBJECTIVES: Most cancer patients want to die at home, but scaleable models to achieve this are not well researched. Our objective was to investigate the temporal association of homecare nursing, especially by generalist nurses, with reduced end-of-life hospitalizations. METHODS: We conducted a retrospective Canadian cohort study of end-of-life cancer decedents during 2004-2009 in Ontario (ON), Nova Scotia (NS), and British Columbia (BC), which have homecare systems that use generalist nurses to provide end-of-life care. Each province linked administrative databases to examine the association during the last six months of life between the homecare nursing rate and the hospitalization rate in the subsequent week, using standardized definitions and controlling for other covariates. We dichotomized nursing into standard and end-of-life care intent. RESULTS: Our cohort included 83,827 cancer decedents. Approximately 55% of decedents were older than 70 and the most common cancer was lung. Nearly 85% of the cohort had at least one hospital admission. Receiving end-of-life compared to standard homecare nursing significantly reduced a patient's hospitalization rate by 34%, 33%, and 17% in ON, BC, and NS. In the last month of life patients having a standard nursing rate of greater than five hours compared to one hour per week had a significantly lower hospitalization rate (relative reduction of 15%-23%) across the three provinces. CONCLUSIONS: Our study showed a protective effect of nursing with an end-of-life intent on hospitalization across the last six months of life and of standard nursing in the last month. This finding's generalizability is strengthened, since the trends were similar across three different homecare systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".