Factors that support home deaths for patients receiving at-home palliative and end-of-life care: a sequential mixed-methods explanatory study
Bibliographic record
Abstract
BACKGROUND: Significant knowledge gaps persist in understanding how to support patients receiving palliative and end-of-life care (PEoLC) who wish to die at home. This study aimed to identify factors associated with remaining at home and home death for patients receiving PEoLC. METHODS: A sequential mixed-methods explanatory study (QUANT + QUAL) was conducted. Administrative data from a not-for-profit at-home palliative care organization in Québec Canada from 2015 to 2024 (n = 5931) and 73 semi-structured interviews with patients receiving PEoLC, caregivers, service providers and decision-makers were collected. Logistic and Cox regression models were completed for the quantitative data analysis. Content analysis was used for the qualitative data analysis. Data integration occurred following the quantitative analyses. RESULTS: The mean age of patients requiring PEoLC was 77.97 (SD: 13.70) years. The sample included 50.3% men and 49.7% women. One quarter (25.8%) of patients receiving PEoLC lived alone. Most patients (93.5%) had cancer. Home death occurred in almost 30% of cases. Over 95% of deaths occurred within 365 days following admission to the at-home palliative care organization. Access to respite care (adjusted odds ratio: 2.699, p < 0.001), female sex, living alone, having been hospitalized, receiving psychological care, and volunteer and transportation support were associated with remaining at home and home deaths for patients receiving PEoLC. The interviews highlighted the importance of respecting the patients' wishes related to end of life and home death. All participants described several challenges to access timely and reliable services following the pandemic. Timely access to home care, nursing and hygiene and a palliative care approach facilitated remaining at home and home death for patients receiving PEoLC. CONCLUSIONS: Respite care more than doubles the odds of remaining at home and home death. Timely access to services that include home care, nursing care and hygiene and a palliative care approach is essential to support patients receiving PEoLC who wish to die at home. Stabilizing staffing of home care teams, standardizing PEoLC services offered across community health centers in the province, and optimal use of nurse practitioners would improve services offered to patients receiving PEoLC and their caregivers.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".