The sociocultural health behavioral model and disparities in colorectal cancer screening among Chinese Americans
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to validate a Sociocultural Health Behavior Model using a structural equation analysis to determine the direction and magnitude of the interdependence of model components in relation to health behavior associated with colorectal cancer (CRC) screening among Chinese Americans. METHODS: A cross-sectional design included a sample of 311 Chinese American men and women age 50 and older. The initial step involved use of confirmatory factor analysis which included the following variables: access/satisfaction with health care, enabling, predisposing, cultural, and health belief factors. Structural equation modeling analyses were conducted on factors for CRC screening. RESULTS: Education and health insurance status were significantly related to CRC screening. Those with less than a high school education and without health insurance were more likely to be "never screened" for CRC than those having more education and health insurance. The path analysis findings also lend support for components of the Sociocultural Health Belief Model and indicated that there was a positive and significant relationship between CRC screening and the enabling factors, between cultural factors and predisposing, enabling, and access/satisfaction with health care factors and between enabling factors and access/satisfaction with health care. CONCLUSIONS: The model highlights the significance that sociocultural factors play in relation to CRC screening and reinforced the need to assist Chinese with poor English proficiency in translation and awareness of the importance of CRC screening. The use of community organizations may play a role in assisting Chinese to enhance colorectal cancer screening rates.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".