Web-Based Skin Cancer Prevention Training for Massage Therapists: Protocol for the Massage Therapists Skin Health Awareness, Referral, and Education Study
Bibliographic record
Abstract
BACKGROUND: Skin cancer, the most common cancer in the United States, is costly and potentially deadly. Its burden can be reduced by early detection and prevention activities. The scope of skin cancer requires going beyond traditional health care providers to promote risk reduction. Partnering with the nonbiomedical workforce, such as massage therapists (MTs), may reach more individuals at risk. MTs see much of their clients' skin and are amenable to performing skin cancer risk reduction activities during massage appointments. OBJECTIVE: The objective of this study is to describe the Massage Therapists Skin Health Awareness, Referral, and Education protocol, presenting an overview of our systematic approach to developing rigorous e-training for MTs to enable them to be partners in skin cancer risk reduction. We also describe procedures for usability and feasibility testing of the training. METHODS: We developed an integrated electronic learning system that includes electronic training (e-training) technology, simulated client interactions, online data collection instruments, and in-person assessment of MTs' application of their training. RESULTS: A total of 20 participants nationally scored the e-training as high for usability and satisfaction. We have screened an additional 77 MTs in Arizona for interest and eligibility, and currently have 37 enrolled participants, of whom 32 have completed the Web-based training. CONCLUSIONS: The structured and rigorous development approach for this skin cancer risk reduction and brief behavioral intervention e-training for MTs begins to fill a gap in skin cancer risk reduction research. Iterative usability testing of our asynchronous Web-based training resulted in positive participant response. Our e-training approach offers greater learner accessibility, increased convenience, and greater scalability than the few existing programs and has the potential to reach many MTs nationally. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13480.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".