Assessment of sun‐safety education behavior via spectrophotometric evaluation: A preliminary study
Bibliographic record
Abstract
BACKGROUND: Biases inherent in self-assessment of sun exposure and sun-safe behavior may lead to inaccurate conclusions about the effectiveness of sun-safety educational programs. OBJECTIVES: We aimed to compare self-reports to objective measures of sun exposure, when examining the effectiveness of passive versus active educational interventions. METHODS: From May to June 2018, 73 participants recruited at a dermatology clinic were sequentially assigned to receive sun-safety education through one of 3 modes: interactive online module, video, or no education. A baseline Sun Exposure and Behavior Inventory (SEBI) questionnaire was administered, and spectrophotometric measurements of sun-exposed and sun-protected areas were taken and reported in the CIE L*a*b* color space. Participants were followed 4-8 and 16 weeks after the initial visit where the SEBI was re-administered, and serial measurements of skin color were taken. The change in SEBI scores and L*a*b values, as calculated by the individual typology angle (ITA°), was analyzed. RESULTS: There was a significant increase in skin darkening in all the groups at 4-8 and 16 weeks follow-up. There was no statistically significant difference between the groups in the magnitude of color change. However, subjectively at 4-8 weeks post-intervention, participants in the interactive module and video groups had significantly improved self-reported SEBI scores compared to control (p < .05, Kruskal-Wallis). By 16 weeks, only the interactive module group showed significant improvement in SEBI scores compared to control (p < .05, ANOVA). CONCLUSION: In determining the effectiveness of sun-safety programs, spectrophotometric evaluation of sun-induced skin pigmentation can allow for a more complete evaluation of self-reported sun exposure and sun-protective behavior.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".