The impact of information about different absolute benefits and harms on intention to participate in colorectal cancer screening: A think-aloud study and online randomised experiment
Bibliographic record
Abstract
BACKGROUND: There is considerable heterogeneity in individuals' risk of disease and thus the absolute benefits and harms of population-wide screening programmes. Using colorectal cancer (CRC) screening as an exemplar, we explored how people make decisions about screening when presented with information about absolute benefits and harms, and how those preferences vary with baseline risk, between screening tests and between individuals. METHOD: We conducted two linked studies with members of the public: a think-aloud study exploring decision making in-depth and an online randomised experiment quantifying preferences. In both, participants completed a web-based survey including information about three screening tests (colonoscopy, sigmoidoscopy, and faecal immunochemical testing) and then up to nine scenarios comparing screening to no screening for three levels of baseline risk (1%, 3% and 5% over 15 years) and the three screening tests. Participants reported, after each scenario, whether they would opt for screening (yes/no). RESULTS: Of the 20 participants in the think-aloud study 13 did not consider absolute benefits or harms when making decisions concerning CRC screening. In the online experiment (n = 978), 60% expressed intention to attend at 1% risk of CRC, 70% at 3% and 77% at 5%, with no differences between screening tests. At an individual level, 535 (54.7%) would attend at all three risk levels and 178 (18.2%) at none. The 27% whose intention varied by baseline risk were more likely to be younger, without a family history of CRC, and without a prior history of screening. CONCLUSIONS: Most people in our population were not influenced by the range of absolute benefits and harms associated with CRC screening presented. For an appreciable minority, however, magnitude of benefit was important.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".