Knowledge, Perceptions and Photoprotective Behaviors Against the Damaging Effects of Direct, Indirect, and Blue Light: There Are No "Cheat Days".
Bibliographic record
Abstract
Objective: We sought to evaluate the impact of the coronavirus-19 (COVID-19) pandemic on sun-seeking and sun-safe behaviors. Methods: We conducted an online, cross-sectional, population-based survey. Results: In total, 1,001 respondents participated in the survey and reported being exposed to 12 or more hours of sunlight (i.e., direct and indirect ultraviolet light, and blue light) each day. Participants self-reported a net increase in all types of light exposure since the onset of the COVID-19 pandemic, especially to blue light (+38%). Notably, while the effects of direct sunlight were well known among survey respondents, they were less aware of the potential damaging impact of indirect sunlight and blue light. Limitations: As the survey was only conducted among residents of the United States, results might not be generalizable to all geographical regions. Conclusion: Social outreach strategies are required to improve sun-safe behaviors. Future behavioral interventions should encourage the implementation of broad-spectrum sun protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".