Canadians’ knowledge of cancer risk factors and belief in cancer myths
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».