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 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".