Theoretically Informed Correlates of Hepatitis B Knowledge among Four Asian Groups: The Health Behavior Framework
Bibliographic record
Abstract
BACKGROUND: Few studies have examined theoretically informed constructs related to hepatitis B (HBV) testing, and comparisons across studies are challenging due to lack of uniformity in constructs assessed. The present analysis examined relationships among Health Behavior Framework factors across four Asian American groups to advance the development of theory-based interventions for HBV testing in at-risk populations. METHODS: Data were collected from 2007-2010 as part of baseline surveys during four intervention trials promoting HBV testing among Vietnamese-, Hmong-, Korean- and Cambodian-Americans (n = 1,735). Health Behavior Framework constructs assessed included: awareness of HBV, knowledge of transmission routes, perceived susceptibility, perceived severity, doctor recommendation, stigma of HBV infection, and perceived efficacy of testing. Within each group we assessed associations between our intermediate outcome of knowledge of HBV transmission and other constructs, to assess the concurrent validity of our model and instruments. RESULTS: While the absolute levels for Health Behavior Framework factors varied across groups, relationships between knowledge and other factors were generally consistent. This suggests similarities rather than differences with respect to posited drivers of HBV-related behavior. DISCUSSION: Our findings indicate that Health Behavior Framework constructs are applicable to diverse ethnic groups and provide preliminary evidence for the construct validity of the Health Behavior Framework.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".