Sociodemographics and their impacts on risk factor awareness and beliefs about cancer and screening: results from a cross-sectional study in Newfoundland and Labrador
Notice bibliographique
Résumé
Abstract Background Our objective was to examine cancer risk factor awareness and beliefs about cancer treatment, outcomes, and screening, and how these are mediated by sociodemographic variables, among Newfoundland and Labrador residents. Methods Participants aged 35 to 74 were recruited through Facebook advertising, and a self-administered online questionnaire was used to collect data. Descriptive statistics, Spearman rank correlations, and multivariate logistic regression analyses were performed. Results Of the 1048 participants who responded and met the inclusion criteria for this study, 1019 were selected for this analysis. Risk factor recognition was generally good, though several risk factors had poor awareness: being over 70 years old (53.4% respondents aware), having a low-fiber diet (65.0%), and drinking more than 1 unit of alcohol per day (62.8%). Our results showed that the participants’ awareness of risk factors was significantly associated with higher income level ( r s = 0.237, P < 0.001), higher education ( r s = 0.231, P < 0.001), living in rural regions ( r s = 0.163, P < 0.001), and having a regular healthcare provider ( r s = 0.081, P = 0.010). Logistic regression showed that among NL residents in our sample, those with higher income, post-secondary education, those in very good or excellent health, and those with a history of cancer all had higher odds of having more positive beliefs about cancer treatment and outcomes. Those with a history of cancer, and those with very good or excellent health, also had higher odds of having more positive beliefs about cancer screening. Finally, compared to Caucasian/white participants, those who were non-Caucasian/white had lower odds of having more positive beliefs about cancer screening. Conclusion Among adults in NL, there was poor awareness that low-fiber diets, alcohol, and age are risk factors for cancer. Lower income and education, rural residence, and not having a health care provider were associated with lower risk factor awareness. We also found a few associations between sociodemographic factors and beliefs about cancer treatment and outcomes or screening. We stress that while addressing awareness is necessary, so too is improving social circumstances of disadvantaged groups who lack the resources necessary to adopt healthy behaviours.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».