A Survey of Licensed Massage Therapists’ Perceptions of Skin Cancer Prevention and Detection Activities
Bibliographic record
Abstract
BACKGROUND: Skin cancer is the most common cancer in the US. Training massage therapists (MTs) in skin cancer prevention and detection creates opportunity for reducing skin cancer burden. Little is known about MTs' perceptions of skin cancer prevention and detection, their discussions of these topics with clients, or their referral recommendations for suspicious skin lesions. PURPOSE: We surveyed MTs' perceptions of their role in engaging in conversations about skin cancer prevention, viewing the skin for suspicious lesions, and referring clients with such lesions to health care providers. SETTING PARTICIPANTS RESEARCH DESIGN: We administered an online survey from 2015-2017 of licensed MTs practicing in the US and at least age 21 years (n = 102); quantitative and qualitative data were analyzed in 2017. MAIN OUTCOME MEASURES: The main variables assessed were MTs perceptions of (a) appropriateness for asking clients about skin cancer history, skin cancer prevention, suspicious lesion referral and follow-up; and (b) comfort with recognizing and discussing suspicious lesions, recommending a client see a doctor for suspicious lesion, and discussing skin cancer prevention. RESULTS: Quantitative data revealed that most MTs were amenable to discussing skin cancer prevention during appointments; few were engaging in these conversations. MTs were more comfortable discussing suspicious lesions and recommending that a client see a doctor than they were sharing knowledge about skin cancer and sun safety. Categories based on qualitative content analysis were: sharing information for the client's benefit, and concerns about remaining within scope of practice. CONCLUSIONS: MTs have boundaries for skin cancer risk-reduction content to include in a client discussion and remain in their scope of practice. These findings will help support a future educational intervention for MTs to learn about and incorporate skin cancer risk-reduction messages and activities into their practice.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".