Sun-protective behaviors in populations at high risk for skin cancer
Bibliographic record
Abstract
Over 3 million new cases of skin cancer are diagnosed in the US annually. Melanoma, a subtype of skin cancer that can be fatal if the disease is not detected and treated at an early stage, is the most common cancer for those aged 25-29 years and the second most common cancer in adolescents and young adults aged 15-29 years. The primary carcinogen for the genesis of skin cancers is ultraviolet light from solar radiation and tanning beds. In spite of massive health campaigns to raise public awareness on ultraviolet radiation, sun-protective practices still fall behind. A plausible explanation is the lack of behavioral change in the populations at risk; in this review article, we examine sun-protective behavior in the four high-risk skin cancer groups: skin cancer survivors, individuals with a family history of melanoma, individuals with physical characteristics associated with skin cancer risk, and organ transplantation patients. Findings in the literature demonstrate that increased knowledge and awareness does not consequently translate into behavioral changes in practice. Behavior can differ as a result of different attitudes and beliefs, depending on the population at risk. Thus, intervention should be tailored to the population targeted. A multidisciplinary health team providing consultation and education is required to influence these much needed changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".