Slip, Slop, Slap, Slide, Seek and Sport: A Systematic Scoping Review of Sun Protection in Sport in Australasia
Bibliographic record
Abstract
Australia and New Zealand have the highest incidence of skin cancer. Sport is a fundamental part of Australasian culture, beginning in childhood, often with life-long participation. Participating in outdoor sports can contribute significantly to the lifetime ultraviolet radiation (UVR) dose individuals receive and their risk of developing skin cancer. This systematic scoping review explores the use of sun-protection by outdoor sporting participants in Australasia and considers how sun-protection practices may be improved and better evaluated in the community. A search of electronic databases using the search strategy "sun protection" AND "sport" AND "Australia" yielded 17 studies published in English from January 1992 to August 2021. Study methods included using UV-dosimeters to measure individual UVR-exposure; remote estimates of clothing-adjusted UVR-exposure; direct observation of sun-protection practices; and self-reported sun-exposure and sun-protection. Despite 40 years of 'Slip, Slop, Slap' campaigns in Australia, the use of sun-protection in most outdoor sports is inadequate. The paucity of comparable data limited our analyses, demonstrating a need for standardized, objective evaluation tools. Such tools, if used across a range of sports, should inform the development of workable recommendations that sporting clubs could implement and adopt into policy, thus empowering them to better protect the health of their participants.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".