Evaluating UV exposure and skin cancer prevention behaviours in Canada: a national population-based cross-sectional study
Bibliographic record
Abstract
Introduction: Cutaneous melanoma is a common cancer with increasing incidence and significant economic burden. Sun-protective behaviours are crucial in addressing UV-related skin cancer risk and are responsive to public health intervention. This study provides a comprehensive overview of recent prevalence and trends in sun protection and UV exposure in Canada. Methods: Using data from the Canadian Community Health Survey (CCHS), we examined sun-protective behaviours and UV exposure in a sample of over 77 000 individuals aged ≥18 years during 2011-2018. The analysis employed multivariable logistic regression, considering factors including age, sex, income and immigration status, with results weighted to reflect the Canadian population. We further evaluated temporal trends in UV exposure and sun-protective practices from 2007 to 2018. Results: Age, sex, income and immigration status significantly influenced sun-protective behaviours. One-third (33.3%) of respondents reported having had a sunburn in the past 12 months, and most reported irregular or 'never use' of sunscreen on their body (64.3%) and face (58.1%). Women had significantly higher odds of using sunscreen on their body and face compared with men (OR 2.85, 95% CI 2.68 to 3.03 and OR 4.22, 95% CI 3.96 to 4.49, respectively). Individuals in the highest income quintile were similarly more likely to use sunscreen on their body and face than those in the lowest income quintile (OR 1.78, 95% CI 1.55 to 2.04 and OR 2.45, 95% CI 2.10 to 2.86, respectively). Temporal trends demonstrated an increasing prevalence of spending 2 hours or more in the sun and a decreasing trend in the use of any sunscreen on the body and face. Conclusions: The findings highlight disparities in sun protection linked to demographic factors. Public health strategies should target high-risk groups to enhance sun-protective behaviours and reduce melanoma incidence. Future interventions must address these disparities to improve skin cancer prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".