Needs of caregivers of patients receiving in-home palliative and end-of-life care
Bibliographic record
Abstract
Home support for patients receiving in-home palliative and end-of-life care (PELC) is greatly dependent on the daily presence of caregivers and their involvement in care delivery. However, the needs of caregivers throughout the care trajectory of a loved one receiving in-home PELC are still relatively unknown. OBJECTIVES AND METHODOLOGY: This descriptive qualitative study focuses on the role of caregivers who have cared for a person receiving in-home PELC with the goal of describing their needs throughout the care trajectory. As part of this process, 20 caregivers took part in semi-directed interviews. RESULTS AND DISCUSSION: This study sheds light on the multiple needs of caregivers of loved ones receiving in-home PELC. These informational, emotional, and psychosocial needs show that caregivers experience changes in their relationship with their loved one. Spiritual needs were expressed through the meaning ascribed to the home support experience. And the practical needs expressed by participants highlight the importance of round-the-clock access to PELC services and the essential importance of nursing support. CONCLUSION: The needs of caregivers of loved ones receiving in-home PELC are not being met to a satisfactory degree. It is important to consider these needs in the care trajectory, alongside the needs of the patients themselves, in order to improve the support experience leading up to the bereavement period.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".