Nurse practitioner and physician end-of-life home visits and end-of-life outcomes
Bibliographic record
Abstract
OBJECTIVES: Physicians and nurse practitioners (NPs) play critical roles in supporting palliative and end-of-life care in the community. We examined healthcare outcomes among patients who received home visits from physicians and NPs in the 90 days before death. METHODS: We conducted a retrospective cohort study using linked data of adult home care users in Ontario, Canada, who died between 1 January 2018 and 31 December 2019. Healthcare outcomes included medications for pain and symptom management, emergency department (ED) visits, hospitalisations and a community-based death. We compared the characteristics of and outcomes in decedents who received a home visit from an NP, physician and both to those who did not receive a home visit. RESULTS: Half (56.9%) of adult decedents in Ontario did not receive a home visit from a provider in the last 90 days of life; 34.5% received at least one visit from a physician, 3.8% from an NP and 4.9% from both. Compared with those without any visits, having at least one home visit reduced the odds of hospitalisation and ED visits, and increased the odds of receiving medications for pain and symptom management and achieving a community-based death. Observed effects were larger in patients who received at least one visit from both. CONCLUSIONS: Beyond home care, receiving home visits from primary care providers near the end of life may be associated with better outcomes that are aligned with patients' preferences-emphasising the importance of NPs and physicians' role in supporting people near the 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".