Predictors of Colon Cancer Screening Among the Saudi Population at Primary Healthcare Settings in Riyadh
Bibliographic record
Abstract
(1) Background: This study aims to identify the sociodemographic, behavioural, and systemic predictors of colorectal cancer (CRC) screening among primary healthcare attendees in Riyadh, Saudi Arabia, to inform targeted interventions and policy strategies. (2) Methods: This cross-sectional study was conducted between March and July 2023 across 48 randomly selected primary healthcare centers in Riyadh, Saudi Arabia. The target population for this study was adults aged 18 and above attending primary healthcare centers in Riyadh. Multi-stage random sampling was used to recruit participants. Multivariate logistic regression was performed to identify independent predictors of CRC screening. (3) Results: CRC screening uptake was found to be only 4.2%. Age was a significant predictor, with individuals aged 50-75 years (adjusted odds ratio [AOR]: 1.90, 95% confidence interval [CI]: 1.50-2.42) and those aged 75 years or older (AOR: 1.37, 95% CI: 1.01-1.87) being more likely to undergo screening compared to younger individuals. Insurance coverage strongly influenced screening behaviour (AOR: 1.64, 95% CI: 1.37-1.96). Smokers were nearly four times more likely to participate in screening than non-smokers (AOR: 3.87, 95% CI: 3.21-4.69), and physical activity was positively associated with screening (AOR: 1.43, 95% CI: 1.11-1.82). (4) Conclusions: CRC screening uptake in Riyadh is critically low, highlighting the need for targeted public health interventions. Key predictors such as age, insurance coverage, smoking, and physical activity underscore the importance of addressing sociodemographic disparities and promoting health awareness. The findings emphasize the need for culturally tailored educational campaigns, improved healthcare access, and enhanced screening programs to increase uptake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".