Where Do Cancer Patients in Receipt of Home-Based Palliative Care Prefer to Die and What Are the Determinants of a Preference for a Home Death?
Bibliographic record
Abstract
Understanding the preferred place of death may assist to organize and deliver palliative health care services. The study aims to assess preference for place of death among cancer patients in receipt of home-based palliative care, and to determine the variables that affect their preference for a home death. A prospective cohort design was carried out from July 2010 to August 2012. Over the course of their palliative care trajectory, a total of 303 family caregivers of cancer patients were interviewed. Multivariate regression analysis was employed to assess the determinants of a preferred home death. The majority (65%) of patients had a preference of home death. The intensity of home-based physician visits and home-based personal support worker (PSW) care promotes a preference for a home death. Married patients, patients receiving post-graduate education and patients with higher Palliative Performance Scale (PPS) scores were more likely to have a preference of home death. Patients reduced the likelihood of preferring a home death when their family caregiver had high burden. This study suggests that the majority of cancer patients have a preference of home death. Health mangers and policy makers have the potential to develop policies that facilitate those preferences.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".