Feasibility and Burden of Lay Caregivers Providing Daily Massages to Patients With Cancer Receiving Hospice and Palliative Care
Bibliographic record
Abstract
Professional massages reduce symptoms experienced by cancer patients, but are costly. A cost-effective way to include this therapy routinely in hospice care is to teach family caregivers to give massages as part of their caregiving activities. However, the burden on caregivers is unknown and might offset patient benefits or cost savings. The pilot study aim was to explore feasibility issues related to licensed massage therapists training caregivers to give massages at home, the burden of giving four daily massages to hospice patients, and feedback about the training and massage delivery. In this pretest/posttest study, caregivers completed the Caregiver Reaction Assessment (CRA), received training on standardized massage techniques from a licensed massage therapist who evaluated their proficiency the following day. Caregivers gave daily massages for 3 days and afterward completed the CRA. Then a researcher interviewed the dyad for feedback about the training and massage delivery. We used paired t tests to evaluate CRA scores and content analysis of interview data. Thirty-nine caregivers (mean age = 46 years, 69% female) completed the study. After training, all but three caregivers provided daily massages. Some caregivers reported minor logistical challenges in massage delivery and documentation, mutual satisfaction, relaxation, and tender moments ranging from laughter and story sharing to closure activities. Mean CRA scores were not significantly different pretest to posttest. We conclude that repeated-dose massages by caregivers to patients dying of cancer is feasible and is worthy of further study to determine the benefits of massage therapy, caregiver and patient experiences, and caregiver burden.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".