Trends in Colorectal Cancer Screening Utilization among Ethnic Groups in California: Are We Closing the Gap?
Bibliographic record
Abstract
BACKGROUND: Given the low prevalence of and racial/ethnic disparities in colorectal cancer screening, it is important to monitor whether prevalence and disparities are increasing or decreasing over time. METHODS: We estimated the prevalence of colorectal cancer screening by year (2001, 2003, and 2005), modality (endoscopy, fecal occult blood test, either), and recency (ever had, up-to-date) for the California population as a whole, major racial/ethnic groups (White, Black, Latino, Asian), and selected Asian subgroups (Chinese, Filipino, Japanese, Korean, Vietnamese) using data from the California Health Interview Survey. All prevalence estimates were age- and gender-standardized. RESULTS: From 2001 to 2005, prevalence of up-to-date screening increased significantly among Whites and Latinos but not among Blacks and Asian Americans. Screening prevalence varied substantially among Asian subgroups, with Korean, Filipino, and Vietnamese Americans having the lowest prevalence. Korean Americans were the only group in the analysis with a significant decline in screening prevalence from 2001 to 2005. The gap between the highest and the lowest up-to-date screening prevalence using any screening modality, exhibited by Japanese and Korean Americans, increased from 18% in 2001 to 30% in 2005. CONCLUSIONS: Findings suggest that we need to intensify efforts to increase colorectal cancer screening, especially among Korean Americans but also among Filipinos, Vietnamese, and Latinos.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".