Couples and Cancer: Feasibility of Brief Instruction in Massage and Touch Therapy to Build Caregiver Efficacy
Bibliographic record
Abstract
This study examined the feasibility of brief instruction in massage and touch therapy for caregivers ("partners") to provide comfort to cancer patients. Fifty partners and 49 patients participated. A longitudinal, within-subjects, repeated measures, control and intervention phases design used self-report instruments to assess feasibility via change in frequency, duration, partner-perceived self-efficacy, and patient-perceived helpfulness over a 90-day follow-up. Exploratory data were collected on psychosocial and quality of life variables. Focus groups provided qualitative data. A structured 6-hour workshop taught basic manual techniques for comfort and relaxation, followed by home practice. Significant increases in frequency (1.2 vs 2.7 times per week) and duration (4.7 vs 12.2 minutes) of massage, both p < .001, were sustained through the 3-month follow-up. Partners' perceived self-efficacy in massage and patients' ratings of its helpfulness more than doubled. Classification tree analysis found caregiver burden, relationship quality, and frequency and duration of practice to predict individual responses. Inhibitions about touch in cancer caregiving may lead to unnecessary physical and emotional distancing at a time when patients need touch the most. Brief instruction may be a feasible intervention to increase caregiver efficacy, patient satisfaction, quality of life, and quality of the relationship.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".