Sociodemographics and their impacts on risk factor awareness and beliefs about cancer and screening: results from a cross-sectional study in Newfoundland and Labrador
Bibliographic record
Abstract
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 (rs = 0.237, P < 0.001), higher education (rs = 0.231, P < 0.001), living in rural regions (rs = 0.163, P < 0.001), and having a regular healthcare provider (rs = 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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".