Increasing Participation Rates in Germany’s Skin Cancer Screening Program (HELIOS): Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: In 2008, a nationwide skin cancer screening (SCS) program was implemented in Germany. However, participation rates remain low. OBJECTIVE: The overall objective of the HELIOS study is to identify subgroup-specific invitation and communication strategies to increase informed SCS participation in Germany. METHODS: Focus group discussions will be performed in Erlangen, Germany, to explore potential invitation and communication strategies as well as possible barriers and motivating factors to participate in SCS. Male and female patients of different age groups who have already been diagnosed with skin cancer, as well as participants without a prior diagnosis of skin cancer, will be invited. Based on these results, an online questionnaire will be developed to identify subgroup-specific invitation strategies. A random sample of 2500 persons from the general population aged >35 years from the Munich area will be contacted to complete the questionnaire. Besides descriptive analysis, multinomial logistic regression will be performed. Additionally, a cluster analysis will be conducted to discover patterns or similarities among the participants. RESULTS: Recruitment for the focus group studies started in February 2021 and is ongoing. As of August 2021, we have enrolled 39 participants. We expect to end enrollment in the qualitative study in September 2021 and to finish the analysis in December 2021. The second part of the study will then start in January 2022. CONCLUSIONS: The results of this project will enable us to derive improved and more efficient invitation and communication strategies for SCS. These may be implemented in the future to facilitate increased SCS uptake and early skin cancer detection. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31860.
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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 | 0.075 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".