The Experiences of Touch Therapies in Symptom Management of Rural and Regional Cancer Patients in Australia
Bibliographic record
Abstract
Introduction: Cancer patients are increasingly combining touch therapies (e.g., remedial massage, lymphatic massage, and/or reflexology) with conventional treatments to deal with the impact of their cancer and treatments on their physical and mental well-being. To understand the impact of integrative oncology services on cancer patients, it is essential to explore the impact that various types of integrative oncology services have on cancer patients. Aims: This paper presents cancer patients' experiences with touch therapies in a community-based cancer support center and to identify opportunities for better access to these practices and service provision in Australia. Methods: A random selection of cancer patients (n=36) receiving touch therapies at a rural/regional community cancer center completed mixed-methods mail surveys regarding the use of touch therapies, their satisfaction, and the impact on pain, fatigue, nausea and overall well-being. Results: Findings indicated that these services helped manage both physical and emotional symptoms. Of the participants experiencing pain and fatigue, findings revealed that touch therapies assisted with pain in 90% of participants and with fatigue in 70%. Conclusion: Given the increased and continued use of touch therapies by individuals with cancer, cancer centers should consider establishing touch therapy services or provide referrals to touch therapy services that can assist with symptom management and improve quality care. By more clearly understanding the benefits of the different types of integrative oncology interventions, patients with cancer receive more tailored and effective interventions throughout of their cancer journey.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".