Family caregiver satisfaction with home-based nursing and physician care over the palliative care trajectory: Results from a longitudinal survey questionnaire
Bibliographic record
Abstract
BACKGROUND: A limited understanding of satisfaction with home-based palliative care currently exists. AIM: This study measured family caregivers' satisfaction with home-based physician and nursing palliative care services, and explored predictors of satisfaction, across the palliative care trajectory. DESIGN: A longitudinal, cohort design was used. Family caregivers were interviewed by telephone by-weekly from palliative care admission until death. Satisfaction was assessed using the Quality of End-of-Life care and Satisfaction with Treatment (QUEST) questionnaire. Multiple logistic regression models were used to determine the extent to which demographic, quality of care, and service related variables predicted satisfaction. SETTING/PARTICIPANTS: Family caregivers (N=104) of palliative care patients. RESULTS: Each of the nine quality of care parameters were consistently found to be significant predictors of overall satisfaction with palliative care. CONCLUSIONS: The results may inform key health policy issues. Specifically, knowledge of how quality of care parameters predict family caregivers' satisfaction over the course of the palliative care trajectory may aid managers responsible for resource allocation and the determination of home care standards.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| 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".