Hospice palliative care volunteers: The benefits for patients, family caregivers, and the volunteers
Bibliographic record
Abstract
OBJECTIVE: Terminally ill patients and family caregivers can benefit greatly from the support and care provided by trained hospice palliative care volunteers. The benefits of doing this kind of volunteer work also extend to the volunteers themselves, who often say they receive more than they give from the patients/families they are "privileged" to be with. The purpose of this article is to demonstrate how hospice palliative care volunteerism benefits both the patients and families who utilize this service as well as the volunteers. METHOD: A review of studies demonstrating how terminally ill patients, and especially family caregivers, can benefit from the use of hospice palliative care volunteers and how the volunteers themselves benefit from their experiences. RESULTS: Terminally ill patients and families receive many benefits from using the services of hospice palliative care volunteers, including emotional support, companionship, and practical assistance (e.g., respite or breaks from caregiving). Volunteering in hospice palliative care also provides many benefits for the volunteers, including being able to make a difference in the lives of others, personal growth, and greater appreciation of what is really important in life. SIGNIFICANCE OF RESULTS: More needs to be done to promote the value of hospice palliative care volunteers to those who can really benefit from their support and care (i.e., patients and their families) as well as to help people recognize the potential rewards of being a hospice palliative care volunteer. It is a win-win situation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".