Development of a Hospital-based Massage Therapy Course at an Academic Medical Center
Bibliographic record
Abstract
BACKGROUND: Massage therapy is offered increasingly in US medical facilities. Although the United States has many massage schools, their education differs, along with licensure and standards. As massage therapy in hospitals expands and proves its value, massage therapists need increased training and skills in working with patients who have various complex medical concerns, to provide safe and effective treatment. These services for hospitalized patients can impact patient experience substantially and provide additional treatment options for pain and anxiety, among other symptoms. The present article summarizes the initial development and description of a hospital-based massage therapy course at a Midwest medical center. METHODS: A hospital-based massage therapy course was developed on the basis of clinical experience and knowledge from massage therapists working in the complex medical environment. This massage therapy course had three components in its educational experience: online learning, classroom study, and a 25-hr shadowing experience. The in-classroom study portion included an entire day in the simulation center. RESULTS: The hospital-based massage therapy course addressed the educational needs of therapists transitioning to work with interdisciplinary medical teams and with patients who have complicated medical conditions. Feedback from students in the course indicated key learning opportunities and additional content that are needed to address the knowledge and skills necessary when providing massage therapy in a complex medical environment. CONCLUSIONS: The complexity of care in medical settings is increasing while the length of hospital stay is decreasing. For this reason, massage provided in the hospital requires more specialized training to work in these environments. This course provides an example initial step in how to address some of the educational needs of therapists who are transitioning to working in the complex medical environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".