Impact of Asian Ethnicity on Colorectal Cancer Screening
Bibliographic record
Abstract
OBJECTIVES: Although research shows that African Americans and Hispanics frequently receive less colorectal cancer screening (CRCS) than whites, few studies have focused on CRCS among Asians. The aims of this study were to compare CRCS between Asians and whites and to evaluate for clinical predictors of CRCS. METHODS: From the 2007 California Health Interview Survey, we identified all Asian and white respondents who were eligible for CRCS. Logistic regression was performed to evaluate for differences in CRCS. We used stratified and interaction analyses to examine whether associations between race and CRCS were modified by insurance status, birthplace, or language skills, while controlling for other confounders. RESULTS: Baseline characteristics were similar between Asians and whites. Only 58% of Asians and 66% of whites reported undergoing up-to-date CRCS (P < 0.01). In multivariate analyses, visiting a physician more than 5 times produced the highest odds of being up-to-date with screening. When compared with whites, Asians had decreased odds of being up-to-date with screening. Stratified analyses showed that this disparity existed mainly in the insured, but not in the uninsured, and it was not modified by place of birth or English language proficiency. CONCLUSIONS: Despite its ability to reduce mortality, CRCS is suboptimal in our US population-based cohort of Asians when compared with whites. A contributing factor to this problem for the Chinese and Koreans may be a lack of awareness regarding CRCS, whereas the source of the problem in the Vietnamese seems to be related to healthcare access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".