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Record W4320917201 · doi:10.3390/curroncol30020177

“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

2023· article· en· W4320917201 on OpenAlexvenueno aff
Luisa Leonie Brokmeier, Katharina Diehl, Bianca Annika Spähn, Charlotte Jansen, Tobias Konkel, Wolfgang Uter, Tatiana Görig

Bibliographic record

VenueCurrent Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsnot available
FundersFriedrich-Alexander-Universität Erlangen-Nürnberg
KeywordsSkin cancerMedicineQualitative researchDermatologyIncidence (geometry)PopulationFamily medicineCancerEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.217
GPT teacher head0.513
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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