Disparities in colorectal cancer screening rates among Asian Americans and non-Latino whites
Bibliographic record
Abstract
Among Asian Americans, colorectal cancer (CRC) is the second most commonly diagnosed cancer, and it is the third highest cause of cancer-related mortality. The 2001 California Health Interview Survey (CHIS 2001) was used to examine 1) CRC screening rates between different Asian-American ethnic groups compared with non-Latino whites and 2) factors related to CRC screening. The CHIS 2001 was a population-based telephone survey that was conducted in California. Responses about CRC screening were analyzed from 1771 Asian Americans age 50 years and older (Chinese, Filipino, South Asian, Japanese, Korean, and Vietnamese). The authors examined two CRC screening outcomes: individuals who ever had CRC screening and individuals who were up to date for CRC screening. For CRC screening, fecal occult blood test (FOBT), sigmoidoscopy/colonoscopy, and any other form of screening were examined. CRC screening of any kind was low in all populations, and Koreans had the lowest rate (49%). Multivariate analysis revealed that, compared with non-Latino whites, Koreans were less likely to undergo FOBT (odds ratio [OR], 0.40; 95% confidence interval [95% CI], 0.25-0.62), and Filipinos were the least likely to undergo sigmoidoscopy/colonoscopy (OR, 0.62; 95% CI, 0.44-0.88) or to be up to date with screening (OR, 0.68; 95% CI, 0.48-0.97). Asian Americans were less likely to undergo screening if they were older, male, less educated, recent immigrants, living with >or= 3 individuals, poor, or uninsured. Asian-American populations, especially Koreans and Filipinos, are under-screened for CRC. Outreach efforts could be more focused on helping Asian Americans to understand the importance of CRC screening, providing accurate information in different Asian languages. Other strategies for increasing CRC screening may include using a more family-centered approach and using qualified translators.
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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 | 0.001 |
| 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) | 0.001 | 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".