Perspectives of international experts and the Danish citizens on the ‘relevant knowledge’ that citizens need for making informed choices about participation in cancer screening: Qualitative study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate the perspectives of international experts and Danish citizens on relevant knowledge about population-based breast, colorectal and cervical cancer screening. METHODS: This was a qualitative interview study with focus group interviews with experts and Danish citizens eligible for breast, colorectal and/or cervical cancer screening. Data were collected using semi-structured interview guides, audio-recorded and transcribed verbatim. A thematic analysis was conducted. RESULTS: Participants were nine international experts from Germany, Canada, the USA, Sweden, the Netherlands and Australia, and 54 citizens from Denmark. Most citizens had 'adequate' or 'problematic' levels of health literacy. Themes that experts and citizens agreed on were: knowledge about the disease and symptoms, practical information about screening, benefits of screening, the option of non-participation and the importance of having numeric information of possible screening outcomes. Experts agreed on the importance of knowledge about the harms of screening, but only a minority of citizens considered this important. CONCLUSIONS: The experts and citizens disagreed on the relevance of knowledge about harms of screening and agreed on other relevant knowledge. PRACTICE IMPLICATIONS: What experts and citizens find important may not align when making informed decisions. Therefore, experts and citizens needs to be involved when developing questionnaires.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".