Self‐reported sun‐related knowledge, attitudes and behaviours among schoolchildren attending <scp>S</scp>outh <scp>A</scp>frican primary schools
Bibliographic record
Abstract
BACKGROUND: Skin cancer and other adverse health effects result from excess solar ultraviolet radiation exposure. Sun protective practices are important interventions for skin cancer prevention, particularly when implemented early in life. Several international studies have assessed children's sun-related knowledge, attitudes and behaviours in school settings but never before in Southern Africa, where multiethnic populations exist. PURPOSE: The purpose of this study is to describe the sun-related knowledge, attitudes and behaviours as self-reported by South African primary schoolchildren and consider the roles of sex and skin type. METHODS: A randomly selected sample of 707 schoolchildren from 24 government, urban schools in all nine provinces of South Africa were surveyed regarding their sun-related knowledge, attitudes and behaviours. RESULTS: Approximately 56% of students reported experience of sunburn last summer and 59% stated that they had got a suntan. Many students (64.5%) believed that one could protect oneself from getting skin cancer by avoiding getting sunburnt. Other means reported to do so by the students were to use sunscreen (65.4%), stay out of the summer sun (48.0%), cover up with clothing (45.5%) and eat the right foods (38.0%). Only about a quarter of the students (22.4%) wrongly agreed that it is safe to get sunburnt once or twice a year. Few students (8.7%) agreed that they like to have a suntan because they feel healthier and agreed that they think a suntan makes them feel more attractive to others (17.3%). Few also agreed that most of their friends (16.1%) and family (14.2%) think that a suntan is a good thing. Children reporting to have white/light brown skin (69.4%) were more likely to agree that they used sunscreen to protect themselves from getting sunburnt compared with children having brown/dark brown/black skin (54.8%) (P = 0.0005). CONCLUSION: South African schoolchildren at urban government schools do have some knowledge about sun protection, and they do have some positive sun behaviours; however, the reported occurrence of sunburn, a risk factor for skin cancer, was relatively high. There were few differences in responses by sex and some differences by skin type. These findings are important for the development of appropriate sun protection programmes aimed at schoolchildren in South Africa and other countries with similar multi-ethnic populations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".