Prevalence and sociodemographic correlates of beliefs regarding cancer risks
Bibliographic record
Abstract
BACKGROUND: Inaccurate beliefs about cancer risk may contribute to unhealthy lifestyle behaviors and poor adherence to recommended screening and prevention guidelines. To address this issue the current study assessed the prevalence and sociodemographic correlates of scientifically unsubstantiated beliefs about cancer risk in a representative sample of the US population. METHODS: Nine hundred fifty-seven US adults with no history of cancer were surveyed by telephone. The survey included 12 statements about cancer risk, risk factors, and prevention that were framed to be contrary to the consensus of current scientific evidence. RESULTS: Participants were inconsistent in their ability to identify the statements as false, and appraisal accuracy was associated with several sociodemographic characteristics. Five of the 12 misconceptions were endorsed as true by at least a quarter of the respondents, and uncertainty was higher than 15% for 7 statements. At the same time, more than two-thirds of the participants were able to identify 7 statements as false and, on average, respondents endorsed fewer than 3 statements as true. Respondents who were male, older, non-White, less educated, and of lower income were most likely to hold inaccurate beliefs. CONCLUSIONS: A notable percentage of the participants in this study hold beliefs about cancer risk at odds with the prevailing scientific evidence. Because the population segments with the least accurate knowledge also bear the greatest burden of cancer, areas for public education and intervention efforts are identified.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".