Evaluation of Indoor Tanning Facilities in American Fitness Centers
Bibliographic record
Abstract
Background Indoor tanning (IT) in fitness facilities encourages a misleading positive relationship between tanning and health. While IT in Canadian fitness facilities has been studied, American literature regarding this topic is lacking. Objectives The objective of this study is to evaluate availability, cost, reported risks, and adherence to legislation of IT in American fitness clubs. Methods This was a cross-sectional study utilizing a telephone questionnaire to survey gyms across all 50 states. The key term “fitness club” was searched in the Yellow Pages and 20 facilities from each state were randomly included into the study. Data were described descriptively and Pearson χ 2 tests were used to compare IT prevalence and rates of noncompliance between population groups. Regression analysis examined potential relationship between cost and prevalence of IT. Results Of the 1000 fitness clubs surveyed, 44.4% (444/1000) offered IT. The overall noncompliance rates for age, rest time, and eye protection were 13.8% (54/390), 26.0% (20/77), and 27.8% (85/225), respectively. The most common risk reported was skin cancer (61.6%), but many facilities were unsure of risks (27.0%) and some described no risk associated with IT (3.2%). The average cost for monthly unlimited tanning was 33 ± 13.96 USD. A state-to-state comparison showed a statistically significant inverse relationship between mean cost and prevalence of IT ( P = .013, [ r]= −0.35). Conclusion The prevalence and noncompliance rates of IT in fitness clubs contradict the healthy lifestyles they are working to promote. To limit harms, legislations should be standardized and more strictly enforced. Additionally, public education on IT risks and the use of higher costs may help minimize IT use.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".