Relationship between sun safety behaviours and modifiable lifestyle cancer risk factors and vitamin D levels
Bibliographic record
Abstract
BACKGROUND: Sun exposure is the most important environmental risk factor for causing skin cancer. PURPOSE: This study examines the relationship between sun protection behaviours and modifiable lifestyle risk factors for other cancers as well as vitamin D levels. METHODS: Cross-sectional data were analysed from two large national health surveys (n = 31, 445 and n = 5604). Sun exposure and protection were characterized by the presence of sunburn, duration of sun exposure, frequency of seeking shade, frequency of wearing a hat and frequency of wearing sunscreen. Using Statistical Analysis System (SAS) software 9.3.1, multivariate logistic regression models were compiled. RESULTS: Unhealthy behaviour practices were associated with sunburns or infrequent sun protection behaviour, such as cigarette consumption (either current or former smokers), second-hand smoke exposure, not having a regular doctor, higher level of alcohol consumption, street drug usage and low levels of fruit/vegetable consumption. Approximately one-quarter of individuals had less than the recommended value of serum vitamin D levels (<50 nmol/L), despite 39.2% of these individuals reporting ≥1 hour of sun exposure. CONCLUSION: Modifiable lifestyle risk factors for other cancers are correlated with infrequently practicing sun protection behaviours for skin cancer prevention. Therefore, cancer prevention campaigns can aim to target all these risk factors associated with different cancers. Sun exposure is not a reliable source to obtain recommended vitamin D levels and that other sources (eg. fish, egg yolk, fortified drinks and supplements) are a safer and more reliable option.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".