Improving Body Mechanics Using Experiential Learning and Ergonomic Tools in Massage Therapy Education
Bibliographic record
Abstract
INTRODUCTION: Current industry data suggest that the rise in occupational injuries for massage therapists is contributing to a significant number leaving the profession after a few short years. While many massage therapists are taught methods for proper body mechanics and self-care within their career educational programs, there are few consistencies in the theoretical approaches to these concepts, even though it is a required component in massage therapy career training. PURPOSE: This study demonstrates a measurable and effective teaching method using a combination of experiential and transformative learning theory models and authentic ergonomics measurement tools to teach effective body mechanics in entry level career training that may be sustainable for new massage therapists entering the field. METHODS: Four cohorts of students (N = 17) enrolled in a kinesiology course for massage therapists were studied using a mixed-methods time series experimental design. A pre- and post-test was conducted by utilizing two industry standard ergonomics risk factor assessment tools as measurable data for score comparison, to denote improvements in each student's risk factor tendencies and provide evidentiary support of learning transfer. Between the pre- and post-test, students participated in a series of experiential learning exercises within class sessions during the semester and completed two reflection journals discussing their experiences. RESULTS: The results showed that there was a statistically significant reduction in ergonomics risk factor scores for all students studied. CONCLUSION: The success of this study demonstrates that the instructional design using experiential and transformative educational theory and general ergonomics concepts is an effective approach to teaching proper body mechanics to massage therapy students which can be adopted into universally accepted curriculum on many levels and could eventually contribute to reduction of occupational injury in the future.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".