Perceived susceptibility to developing cancer and screening for colorectal and prostate cancer: A longitudinal analysis of Alberta’s Tomorrow Project
Bibliographic record
Abstract
INTRODUCTION: To assess the association between perceived susceptibility of developing cancer and being screened via sigmoidoscopy/colonoscopy and prostate-specific antigen, respectively. METHODS: Participants aged 35-69, who resided in Alberta, Canada, were enrolled into the study between 2000 and 2008. We used general linear mixed models, adjusted for age, marital status, work status, education, family history and place of residence, to explore the association. RESULTS: Perceived susceptibility of developing cancer was associated with both screening tests at baseline and a maximum of 14-year follow-up: (i) colorectal cancer screening - adjusted odds ratios were 1.97 (95% CI = 1.52-2.55) per one-unit increase in participants' personal belief in susceptibility to cancer, and 1.03 (95% CI = 1.00-1.04) per one-percent increase in participants' estimate of their own chance of developing cancer; (ii) prostate cancer screening - adjusted odds ratios were 1.36 times greater (95% CI = 1.07-1.72), and 1.02 times higher (95% CI = 1.01-1.03), for each respective perceived susceptibility measure. CONCLUSION: Health promotion can focus on targeting and heightening personal perceived susceptibility of developing cancer in jurisdictions with low screening rates for colorectal or prostate cancer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".