Promoting culturally appropriate colorectal cancer screening through a health educator
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer mortality in the US. Surveys reveal low CRC screening levels among Asians in the US, including Chinese Americans. METHODS: A randomized controlled trial was conducted with Chinese patients to evaluate a clinic-based, culturally and linguistically appropriate intervention promoting fecal occult blood test (FOBT) screening. The multifaceted intervention included a trilingual and bicultural health educator, bilingual materials (a video, a motivational pamphlet, an informational pamphlet, and FOBT instructions), and three FOBT cards. Patients in the control arm received usual care. Our primary outcome measure was FOBT screening within 6 months after randomization. The proportion of FOBT completion in the intervention and control arms was compared by using a chi-square test, and logistic regression analysis was performed to adjust for the effects of sociodemographic variables and prior screening history. Potential effect modifications were also tested by using logistic regression models. RESULTS: Our intervention had a strong effect on FOBT completion (intervention group, 69.5%; control group, 27.6%), and the adjusted odds of FOBT slightly increased to over 6-fold greater in the intervention arm compared with the control arm. No effect modification by age, gender, language, insurance, or prior FOBT was found. CONCLUSIONS: The authors' multifaceted, culturally appropriate intervention significantly increased FOBT screening in a group of low-income and less-acculturated minority patients. Given the large effect size, future research should determine the effective core component(s) that can increase CRC screening in both the general and minority populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".