Using More End-of-Life Homecare Services is Associated With Using Fewer Acute Care Services
Bibliographic record
Abstract
BACKGROUND: Healthcare systems are investing in end-of-life homecare to reduce acute care use. However, little evidence exists on the timing and amount of homecare services necessary to reduce acute care utilization. OBJECTIVES: To investigate whether admission time to homecare and the amount of services, as measured by average nursing and personal support and homemaking (PSH) hours/week (h/wk), are associated with using acute care services at end-of-life. RESEARCH DESIGN: Retrospective observational cohort study. SUBJECTS: Decedents admitted to end-of-life homecare in Ontario, Canada. MEASURES: The odds ratios (OR) of having a hospitalization or emergency room visit in the 2 weeks before death and dying in a hospital. RESULTS: The cohort (n = 9018) used an average of 3.11 (SD = 4.87) nursing h/wk, 3.18 (SD = 6.89) PSH h/wk, and 18% were admitted to homecare for <1 month. As admission time to death and homecare services increased, the adjusted OR of an outcome decreased in a dose response manner. Patients admitted earlier than 6 months before death had a 35% (95% CI: 25%-44%) lower OR of hospitalization than those admitted 3 to 4 weeks before death; patients using more than 7 nursing h/wk and more than 7 PSH h/wk had a 50% (95% CI: 37%-60%) and 35% (95% CI: 21%-47%) lower OR of a hospitalization, respectively, than patients using 1 h/wk, controlling for other covariates. Other outcomes had similar results. CONCLUSION: These results suggest that early homecare admission and increased homecare services will help alleviate the demand for hospital resources at end-of-life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".