Factors associated with home death for individuals who receive home support services: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To determine the factors associated with a home death among older adults who received palliative care nursing home services in the home. METHODS: The participants in this retrospective cohort study were 151 family caregivers of patients who had died approximately 9 months prior to the study telephone interview. The interview focused on the last year of life and covered two main areas, patient characteristics and informal caregiver characteristics. RESULTS: Odds ratios [OR] and 95% confidence intervals [95% CI] were used to determine which of the 15 potential informal caregiver and seven patient predictor variables were associated with dying at home. Multivariate analysis revealed that the odds of dying at home were greater when the patient lived with a caregiver [OR = 7.85; 95% CI = (2.35, 26.27)], the patient stated a preference to die at home [OR= 6.51; 95% CI = (2.66,15.95)], and the family physician made home visits [OR = 4.79; 95% CI = (1.97,11.64)]. However the odds were lower for patients who had caregivers with fair to poor health status [OR = 0.22; 95% CI = (0.07, 0.65)] and for patients who used hospital palliative care beds [OR = 0.31; 95% CI = (0.12, 0.80)]. DISCUSSION: The findings suggest that individuals who indicated a preference to die at home and resided with a healthy informal caregiver had better odds of dying at home. Home visits by a family physician were also associated with dying at home.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".