Canadians’ knowledge of cancer risk factors and belief in cancer myths
Bibliographic record
Abstract
BACKGROUND: Many untrue statements about cancer prevention and risks are circulating. The objective of this study was to assess Canadians' awareness of known cancer risk factors and cancer myths (untruths or statements that are not completely true), and to explore how awareness may vary by sociodemographic and cognitive factors. METHODS: Cancer myths were identified by conducting scans of published, grey literature, and social media. Intuitive-analytic thinking disposition scores included were actively open- and close-minded thinking, as well as preference for intuitive and effortful thinking. A survey was administered online to participants aged 18 years and older through Prolific. Results were summarized descriptively and analyzed using chi-square tests, as well as Spearman rank and Pearson correlations. RESULTS: Responses from 734 Canadians were received. Participants were better at identifying known cancer risk factors (70% of known risks) compared to cancer myths (49%). Bivariate analyses showed differential awareness of known cancer risk factors (p < 0.05) by population density and income, cancer myths by province, and for both by ethnicity, age, and all thinking disposition scores. Active open-minded thinking and preference for effortful thinking were associated with greater discernment. Tobacco-related risk factors were well-identified (> 90% correctly identified), but recognition of other known risk factors was poor (as low as 23% for low vegetable and fruit intake). Mythical cancer risk factors with high support were consuming additives (61%), feeling stressed (52%), and consuming artificial sweeteners (49%). High uncertainty of causation was observed for glyphosate (66% neither agreed or disagreed). For factors that reduce cancer risk, reasonable awareness was observed for HPV vaccination (60%), but there was a high prevalence in cancer myths, particularly that consuming antioxidants (65%) and organic foods (45%) are protective, and some uncertainty whether drinking red wine (41%), consuming vitamins (32%), and smoking cannabis (30%) reduces cancer risk. CONCLUSIONS: While Canadians were able to identify tobacco-related cancer risk factors, many myths were believed and numerous risk factors were not recognized. Cancer myths can be harmful in themselves and can detract the public's attention from and action on established risk factors.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".