What Variables Contribute to the Achievement of a Preferred Home Death for Cancer Patients in Receipt of Home-Based Palliative Care in Canada?
Bibliographic record
Abstract
BACKGROUND: Home is often deemed to be the preferred place of death for most patients. Knowing the factors related to the actualization of a preferred home death may yield evidence to enhance the organization and delivery of healthcare services. OBJECTIVE: The objectives of this study were to measure the congruence between a preferred and actualized home death among cancer patients in receipt of home-based palliative care in Canada and explore predictors of actualizing a preferred home death. METHODS: A longitudinal prospective cohort design was conducted. A total of 290 caregivers were interviewed biweekly over the course of patients' palliative care trajectory between July 2010 and August 2012. Cross-tabulations and multivariate analyses were used in the analysis. RESULTS: Home was the most preferred place of death, and 68% of patients who had voiced a preference for home death had their wish fulfilled. Care context variables, such as living with others and the intensity of home-based nursing visits and hours of care provided by personal support workers (PSW), contributed to actualizing a preferred home death. The intensity of emergency department visits was associated with a lower likelihood of achieving a preferred home death. CONCLUSIONS: Higher intensity of home-based nursing visits and hours of PSW care contribute to the actualization of a preferred home death. IMPLICATIONS FOR PRACTICE: This study has implications for policy decision-makers and healthcare managers. Improving and expanding the provision of home-based PSW and nursing services in palliative home care programs may help patients to actualize a preferred home death.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".