Explaining disparities in colorectal cancer screening among five Asian ethnic groups: A population-based study in California
Bibliographic record
Abstract
BACKGROUND: Data from the California Health Interview Survey (CHIS) indicate that levels and temporal trends in colorectal cancer (CRC) screening prevalence vary among Asian American groups; however, the reasons for these differences have not been fully investigated. METHODS: Using CHIS 2001, 2003 and 2005 data, we conducted hierarchical regression analyses progressively controlling for demographic characteristics, English proficiency and access to care in an attempt to identify factors explaining differences in screening prevalence and trends among Chinese, Filipino, Vietnamese, Korean and Japanese Americans (N = 4,188). RESULTS: After controlling for differences in gender and age, all Asian subgroups had significantly lower odds of having ever received screening in 2001 than the reference group of Japanese Americans. In addition, Korean Americans were the only subgroup that had a statistically significant decline in screening prevalence from 2001 to 2005 compared to the trend among Japanese Americans. After controlling for differences in education, marital status, employment status and federal poverty level, Korean Americans were the only group that had significantly lower screening prevalence than Japanese Americans in 2001, and their trend to 2005 remained significantly depressed. After controlling for differences in English proficiency and access to care, screening prevalences in 2001 were no longer significantly different among the Asian subgroups, but the trend among Korean Americans from 2001 to 2005 remained significantly depressed. Korean and Vietnamese Americans were less likely than other groups to report a recent doctor recommendation for screening and more likely to cite a lack of health problems as a reason for not obtaining screening. CONCLUSIONS: Differences in CRC screening trends among Asian ethnic groups are not entirely explained by differences in demographic characteristics, English proficiency and access to care. A better understanding of mutable factors such as rates of doctor recommendation and health beliefs will be crucial for designing culturally appropriate interventions to promote CRC screening.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".