“Well, to Be Honest, I Don’t Have an Idea of What It Might Be”—A Qualitative Study on Knowledge and Awareness Regarding Nonmelanoma Skin Cancer
Bibliographic record
Abstract
Nonmelanoma skin cancer (NMSC) is the most common cancer type in Western industrialized countries. However, research into the knowledge and awareness in the general population regarding NMSC is still scarce. This qualitative study aims to fill this research gap. Face-to-face, semi-structured interviews with 20 individuals aged 55-85 years were conducted between February and October 2020. Transcribed interviews were analyzed using qualitative content analysis. The term "white skin cancer"-the German colloquial term of NMSC-was well-known, but the incidence was underestimated. None of the participants could give a precise definition of NMSC, and various alterations in the skin were, partially wrongly, stated as potential signs for NMSC. As risk factors for NMSC, solar radiation, and fair skin type were mentioned most often. The perceived individual risk of developing NMSC and risk compared to individuals of the same age and gender were low in our sample. Own knowledge about NMSC was mostly perceived to be insufficient, and the majority of the sample would like to receive more information on NMSC. Our results emphasize a need to inform about the signs and risks of NMSC not only in the studied older age group but also in younger people.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".