Vitamin D and Sun Exposure: A Community Survey in Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sun exposure carries both harms and benefits. Exposing the skin to the sun is the main modifiable cause of skin cancers, which exert a considerable health and economic burden in Australia. The most well-established benefit of exposure to ultraviolet (UV) radiation is vitamin D production. Australia has the highest incidence of skin cancer in the world but, despite the high ambient UV radiation, approximately one quarter of the population is estimated to be vitamin D deficient. Balancing the risks and benefits is challenging and requires effective communication. We sought to provide a snapshot of public knowledge and attitudes regarding sun exposure and vitamin D and to examine the associations between these factors and sun protective behaviors. In 2020 we administered an online survey; 4824 participants with self-reported fair or medium skin color were included in this analysis. Only 25% and 34% of participants were able to identify the amount of time outdoors needed to maintain adequate vitamin D status in summer and winter, respectively and 25% were concerned that sunscreen use inhibits vitamin D synthesis. This lack of knowledge was associated with suboptimal sun protection practices. Public education is warranted to prevent over-exposure, while supporting natural vitamin D production.
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.
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) | 0.000 | 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 | 0.001 |
| 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 it