Congruence between Preferred and Actual Place of Death for Those in Receipt of Home-Based Palliative Care
Bibliographic record
Abstract
Background: Understanding the factors that affect the congruence between preferred and actual place of death may help providers offer clients customized end-of-life care settings. Little is known about this congruence for cancer patients in receipt of home-based palliative care. Objectives: This study aims to determine the congruence between preferred and actual place of death among cancer patients in home-based palliative care programs. Design: A longitudinal prospective cohort study was conducted. Congruence between preferred and actual place of death was measured. Both univariate and multivariate analyses were used to assess the determinants of achieving a preferred place of death. From July 2010 to August 2012, a total of 290 caregivers were interviewed biweekly over the course of their palliative care trajectory from entry to the program and death. Results: The overall congruence between preferred and actual place of death was 71.72%. Home was the most preferred place of death. The intensity of home-based nursing visits and hours of care from personal support workers (PSWs) increased the likelihood of achieving death in a preferred setting. Conclusions: The provision of care by home-based nurse visits and PSWs contributed to achieving a greater congruence between preferred and actual place of death. This finding highlights the importance of formal care providers in signaling and executing the preferences of clients in receipt of home-based palliative care.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".