Likelihood of Dermatology Patients to Inquire about Sun Protection Measures during a Regular Clinic Visit
Bibliographic record
Abstract
BACKGROUND: Health information-seeking behavior refers to ways individuals acquire health information. Few studies demonstrate its association with skin cancer. OBJECTIVES: To determine the likelihood of patients to inquire about sun protection methods from dermatologists and whether such knowledge will affect their preventive behaviors. METHODS: Over a 2-month period, 396 dermatology patients completed a survey assessing their sun awareness knowledge and inquiry about sun protection measures. Features associated with increased skin cancer risk were correlated with patients' inquiry about sun protection information and their future intentions to practice safe sun behavior. RESULTS: One in seven patients (13.8%) inquired about sun protection measures. Patients with features known to be associated with increased skin cancer risk infrequently inquired about sun protection methods (4.5-23.8%). Post-physician counseling, patients with previous sun awareness education had a greater intention to use sunscreen routinely (41.4% vs 28.3%, p = .01), avoid the sun (31.2% vs 18.2%, p = .004), and wear sun-protective clothing (32.5% vs 23.2%., p = .05). CONCLUSION: Patients' desire for medical information from physicians does not translate efficiently into seeking sun awareness information from dermatologists. Dermatologists should play a greater proactive role in skin cancer prevention through initiation of regular personalized discussions on sun awareness issues.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".