Adherence to Cancer Prevention Guidelines among Older White and Black Adults in the Health ABC Study
Bibliographic record
Abstract
One-third of cancers can be prevented through healthy lifestyles. This study investigates the prevalence of and factors associated with engagement in cancer prevention guidelines in a population-based cohort of 2124 older white and black men and women. We used Health ABC data to construct a score from 0 (lowest adherence) to 7 (greatest adherence) based on the sum of seven recommendations for cancer prevention from the World Cancer Research Fund/American Institute for Cancer Research; body fatness (maintenance of healthy body weight), physical activity (at least moderately physically active), diet (fruit, vegetables, fiber, and red and processed meat), and alcohol. Mean (SD) scores in men and women were 3.24 (1.09) and 3.17 (1.10). Lower scores were associated with younger age (women only), black race, current smoking, and prevalent cardiovascular disease. Less than 1% of men and women adhered to all recommendations. Of the individual guidelines, adherence was lowest for fiber (9% of men; 6% of women) followed by physical activity (26% of men; 18% of women), and body weight (21% of men; 26% of women). These results suggest a critical public health need, especially given the growing older population. Black older adults, smokers, and those with prevalent disease may be at higher risk and thus warrant additional focus.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".