Integration of Massage Therapy in Outpatient Cancer Care
Bibliographic record
Abstract
BACKGROUND: Massage therapy can be helpful in alleviating cancer-related symptoms and cancer treatment-related symptoms. While surveys have noted that cancer patients seek out massage as a nonpharmacologic approach during cancer treatment, little is known about the integration of massage in outpatient cancer care. PURPOSE: The purpose of this study was to examine the extent to which massage is being integrated into outpatient cancer care at NCI-designated Cancer Centers. SETTING: This study used descriptive methods to analyze the integration of massage in NCI-designated Cancer Centers providing clinical services to patients (n = 62). DESIGN: Data were collected from 91.1% of the centers (n = 59) using content analysis and a telephone survey. A dataset was developed and coded for analysis. MAIN OUTCOME MEASURE: The integration of massage was assessed by an algorithm that was developed from a set of five variables: 1) acceptance of treatment as therapeutic, 2) institution offers treatment to patients, 3) clinical practice guidelines in place, 4) use of evidence-based resources to inform treatment, and 5) shared knowledge about treatment among health care team. All centers were scored against all five variables using a six-point scale, with all variables rated equally. RESULTS: The integration of massage ranged from not at all (0) to very high (5) with all five levels of integration evident. Only 11 centers (17.7% of total) rated a very high level of integration; nearly one-third of the centers (n = 22) were found to have no integration of massage at all-not even provision of information about massage to patients through the center website. CONCLUSIONS: The findings of this analysis suggest that research on massage is not being leveraged to integrate massage into outpatient cancer care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".