Sun exposure behaviours as a compromise to paradoxical injunctions: Insight from a worldwide survey
Bibliographic record
Abstract
BACKGROUND: Behavioural interventions can improve attitudes towards sun protection but the impact remains inconsistent worldwide. OBJECTIVE: To assess awareness of and attitudes towards the multiple facets of sun exposure and suggest ways to improve prevention from overexposure to the sun in all geographical zones and multiple skin types. METHODS: Online survey was conducted from 28 September to 18 October 2021. Study population was selected from the Ipsos online Panel (3,540,000 panellists), aged ≥18 years, from 17 countries around the five continents. Demographics, sun-exposure habits and practices, understanding of risks and information on phototypes were documented and analysed using descriptive statistics. RESULTS: Eighty-eight per cent of participants knew that sunlight can cause skin health problems (90% phototypes I-II, 82% phototypes V-VI, >90% in American and European countries, 72% in Asia and 85% in Africa). Eighty-five per cent used some form of protection against sunlight, predominantly: Seeking shade (77%), avoiding the midday sun (66%), facial application of sunscreen (60%) and wearing protective clothing (44%). The perception of sunlight itself is positive ('it gives energy' for 82%; 'tanned skin looks attractive' for 72%), although less in Asian countries and among individuals with dark skin phototypes. Eighty-three per cent reported having experienced sunburn, mainly in Australia, Canada, USA, Germany, France and Russia, and among individuals with dark skin phototypes. Only 12% systematically/often used all types of protection during exposure to the sun and 23% believed it is safe to go out in the sun with no protection when their skin is already tanned. From 13% (skin phototype I) to 26% (phototype VI) reported not using any form of protection against the sun. Knowledge and habits were significantly superior among people who are accustomed to seeing a dermatologist for a complete skin exam. CONCLUSIONS: Dermatologists could play a crucial role in relaying novel prevention messages, more finely tailored to specific risks, populations and areas of the world.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".