Rural–urban disparities in colorectal cancer screening among military service members and Veterans
Bibliographic record
Abstract
Introduction: Little is known about rural–urban disparities in colorectal cancer (CRC) screening rates among the military service member and Veteran (SMV) population in the United States. Given that health care access is a challenge in rural areas, we sought to determine whether rural-dwelling Veterans were less likely to be screened for CRC than urban-dwelling Veterans. Methods: Secondary data for this cross-sectional study were retrieved from the 2016 Behavioral Risk Factor Surveillance System for a national sample of non-institutionalized SMVs ( N = 63,919). The influence of rurality on CRC screening among SMVs was determined using maximum likelihood multiple logistic regression. Results: After controlling for relevant covariates, rurality was independently associated with decreased likelihood of meeting guidelines for CRC screening among SMVs (odds ratio = 0.83, 95% confidence interval, 0.76–0.90). Discussion: Innovative interventions for CRC screening should target SMVs in rural areas because doing so may lower mortality from CRC.
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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 | 0.000 | 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 | 0.000 | 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) | 0.004 | 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".