The Impact of 4 Risk Communication Interventions on Cancer Screening Preferences and Knowledge
Bibliographic record
Abstract
PURPOSE: The US Preventive Services Task Force has changed their screening recommendations, encouraging informed patient choice and shared decision making as a result of emerging evidence. We aimed to compare the impact of a didactic intervention, a descriptive harms intervention, a narrative intervention, and a new risk communication strategy titled Aiding Risk Information learning through Simulated Experience (ARISE) on preferences for a hypothetical beneficial cancer screening test (one that reduces the chance of cancer death or extends life) versus a hypothetical screening test with no proven physical benefits. METHOD: A total of 3386 men and women aged 40 to 70 completed an online survey about prostate or breast cancer screening. Participants were randomly assigned to either an unbeneficial test condition (0 lives saved due to screening) or a beneficial test condition (1 life saved due to screening). Participants then reviewed 4 informational interventions about either breast (women) or prostate (men) cancer screening. First, participants were provided didactic information alongside an explicit recommendation. This was followed by a descriptive harms intervention in which the possible harms of overdetection were explained. Participants then viewed 2 additional interventions: a narrative and ARISE (an intervention in which participants learned about probabilities by viewing simulated outcomes). The order of these last 2 interventions was randomized. Preference for being screened with the test and knowledge about the test were measured. RESULTS: With each successive intervention, preferences for screening tests decreased an equivalent amount for both a beneficial and unbeneficial test. Knowledge about the screening tests was largely unimpacted by the interventions. CONCLUSIONS: Presenting detailed risk and benefit information, narratives, and ARISE reduced preferences for screening regardless of the net public benefit of screening.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".