The gap between knowledge and undergoing colorectal cancer screening using the Health Belief Model: A national survey
Bibliographic record
Abstract
BACKGROUND/AIMS: Colorectal cancer (CRC) is a public health issue, and before the initiation of a national cancer screening program, there is a need to examine the acceptance of the public to undergo CRC screening and explore potential barriers. MATERIALS AND METHODS: A nationwide survey was conducted using an electronic platform to collect demographic variables and using the Health Belief Model to assess attitudes and behavior of participants as well as the knowledge about and intent to undergo CRC screening. At the end of the survey, participants from Riyadh were invited to get screened for CRC. RESULTS: Responses from 5720 individuals covering all the 13 jurisdictions of Saudi Arabia were collected. Males represented 71.53% of the respondents; the mean age was 43.28 years and 15.24% had already undergone CRC screening using various methods, mostly colonoscopy (72.73%). The mean knowledge score was 11.05 (standard deviation 4.4, range 1-23), with no difference between genders, jurisdictions of the Kingdom, between those who expressed interest in screening and those who did not, and between those who accepted the invitation to undergo CRC screening and those who did not. Participants displayed positive attitudes toward both CRC screening and colonoscopy as a screening tool, and 73% expressed willingness to undergo screening. On multivariate analysis, male gender was the only factor associated with a higher probability of accepting screening, whereas neither knowledge nor willingness to undergo screening predicted accepting the invitation to screening. CONCLUSION: Although the majority of participants were willing to undergo screening, no significant correlation between knowledge and willingness to undergo screening were predictors of screening uptake. Other areas that could be targeted in the promotion of CRC screening uptake to bridge the gap between "knowing" and "doing" should be explored.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".