Serial Cross‐Sectional Observations of Sun‐Protective Behaviors at an Annual Outdoor Motorsport Event in Tropical Queensland, Australia
Bibliographic record
Abstract
Skin cancer, the most prevalent cancer in Caucasians residing at low latitudes, can primarily be prevented by avoiding overexposure to sunlight. Serial cross-sectional observations were conducted at an outdoor motorsport event held in Townsville, Queensland each July (Southern winter) to determine whether sun-protection habits changed over time. Most (71.1%) of the 1337 attendees observed (97.6% lightly pigmented skin, 64.0% male) wore a hat (any style shading the face), while few (18.5%) wore three-quarter or full-length sleeves. While hat-wearing rates (any style) were similar in 2009 (326, 72.6%) and 2013 (625, 70.4%), the use of sun-protective styles (wide-brimmed/bucket/legionnaires) decreased from 29.2% to 18.6% over the same period, primarily because the use of sun-protective hats halved (from 28.7% to 14.0%) among females, while decreasing from 29.4% to 21.1% in males. Although relatively few individuals wore sun-protective (three-quarter-length or full-length) sleeves regardless of year (OR = 0.117, P < 0.0001), encouragingly, the use of sun-protective sleeves more than doubled between 2009 (10.5%) and 2013 (22.5%). Interestingly females, albeit the minority, at this sporting event were less likely to wear a hat (OR = 0.473, P < 0.0001) than males. These findings highlight the need for continued momentum toward skin cancer primary prevention through sun protection with a dedicated focus on outdoor sporting settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".