An international comparison of Google searches for <i>sunscreen, sunburn, skin cancer,</i> and <i>melanoma</i>: Current trends and public health implications
Bibliographic record
Abstract
BACKGROUND: (GT) offers insight into public interests and behaviors and holds potential for guiding public health campaigns. OBJECTIVES: This study explored international trends in English-speaking countries (United States, United Kingdom, Canada, Australia, New Zealand) in searches for sunscreen, sunburn, skin cancer, and melanoma to better guide skin cancer prevention campaigns. METHODS: was queried for search terms from January 1, 2004 to December 31, 2017. Respective national databases were queried for melanoma outcome data from 2004 to 2014 and compared with time-matched search data. Correlations between search terms, time, and melanoma outcomes were assessed for each country. Quantitative analyses were performed to evaluate differences in search volumes between countries with varying melanoma incidence. RESULTS: In all countries, the strongest intra-term correlation was between sunscreen and sunburn. Searches for sunscreen and sunburn are increasing for all countries. For all countries except the United Kingdom and New Zealand, searches for skin cancer and melanoma are decreasing for one or both terms. Correlations between search terms and melanoma outcomes were variable and specific to each country. Quantitative analysis revealed that countries with higher melanoma incidence had higher search volumes for all terms. Search volumes were especially high for skin cancer and melanoma in Australia compared with other countries. Comparisons between moderate melanoma incidence countries were less clear. CONCLUSIONS: Online skin cancer prevention campaigns should focus on the search terms sunburn and sunscreen, especially given the declining interest between 2004 and 2016 in the terms skin cancer and melanoma seen in multiple countries. Search term interests varied with melanoma outcomes and between countries, suggesting the importance of customizing approaches based on local population interests and geographic areas.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".