Medical Provider Recommendations to Massage Therapy: a Card Study
Bibliographic record
Abstract
BACKGROUND: Communication between massage therapy patients and their medical providers has not been widely described, especially with respect to health care in the United States. PURPOSE: To examine which type of medical providers recommend massage therapy (MT), and how often massage therapy patients tell their providers about their treatment. SETTING: Independent massage therapy practices in a Practice-based Research Network (PBRN) in Northeast Ohio. PARTICIPANTS: 21 licensed massage therapists (LMT). RESEARCH DESIGN: A cross-sectional descriptive study. For consecutive, nonrepeating visits to their practices, each LMT completed up to 20 cards with information on the patient and visit. Analysis compared visits for patients based on whether they reported telling their health provider about their use of MT or being recommended for massage by a health provider. RESULTS: Among 403 visits to 21 LMTs, 51% of patients had told their primary care clinician about seeing an LMT, and for 23%, a health-care provider had recommended visiting an LMT for that visit. Patients who told their primary care provider that they use massage therapy were more likely to be established patients, or to be seen for chronic pain complaints. Visits recommended by a physician were more likely to be for chronic conditions. CONCLUSION: Patients who are established in the massage practice and those receiving massage for a specific condition are more likely to tell their primary care provider that they use massage and are also more likely to have been recommended for massage by a health-care provider. This information will help LMTs target and inform patients about the importance of talking with their health-care providers about their use of massage, and provide LMTs with a starting point of which types of health-care providers already recommend massage. This information will further open the dialogue about the integration of massage therapy in conventional health 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 | 0.008 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".