The effects of cancer beliefs and sociodemographic factors on colorectal cancer screening behavior in Newfoundland and Labrador
Bibliographic record
Abstract
Abstract Background This study investigated the beliefs about cancer treatment, outcomes, and screening among older adults ages 50–74 in Newfoundland and Labrador and whether these beliefs or sociodemographic factors were associated with differences in colorectal cancer (CRC) screening behavior. Methods This analysis uses data collected online survey of adults on cancer awareness and prevention in NL. Chi-square tests were used to assess whether there were differences in distributions of beliefs based on CRC screening behaviour. Logistic regression was used to identify sociodemographic factors independently associated with CRC screening behavior. Results Most people held positive beliefs about cancer outcomes and treatment, though only 57.36% ever had CRC screening. Interestingly, 56.5% of participants believed that cancer treatment is worse than cancer itself. However, no beliefs about cancer treatment and outcomes were associated with CRC screening behavior. People who never had CRC screening were more likely to believe that: their worries about what might be found would prevent them from screening (χ2 = 9.380, P = .009); screening is only necessary if they have symptoms (χ2 = 15.680, P < .001); screening has a high risk of leading to unnecessary surgery (χ2 = 6.824, P = .032). Similarly, people who never had CRC screening were less likely to believe that regular screening would give them a feeling of control over their health (χ2 = 12.255, P = .002). Logistic regression identified that men had higher odds of having had CRC screening than women in our study (OR(95% CI):1.689(1.135–2.515)), as did all other age groups compared to people ages 50–54. No differences were found in CRC screening behaviour based on ethnicity, BMI classification, geography, education, income, or history of cancer in self, or history of cancer in a first-degree relative. Conclusion Although the majority NL residents in our sample held positive beliefs towards cancer screening, treatment and outcomes, only just over half of participants have ever had CRC screening. This discordance should be investigated further. Participants with more negative beliefs about screening were more likely to have never participated in CRC screening. Our findings further suggest that NL’s CRC screening program is equitably reaching people from different socioeconomic backgrounds, though we observed a disparity in participation among genders in our sample.
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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) | 0.003 | 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".