Health Communication Patterns and Adherence to a Micronutrient Home Fortification Program in Rural China
Bibliographic record
Abstract
OBJECTIVES: Examine the association between ethnic health communication patterns and adherence to a micronutrient home fortification program in rural China among 3 distinct ethnic groups. DESIGN: Cross-sectional survey conducted in rural western China. SETTING: Enrolled 283 villages across 6 national poverty counties in rural western China. PARTICIPANTS: A total of 1,358 caregiver-children pairs with Han, Tibetan, or Yi ethnic backgrounds. VARIABLES MEASURED: A structured questionnaire was used to collect information on caregiver demographics, program adherence, and health communication about the program. ANALYSIS: Logistic regression model was used to examine the associations between health communication patterns and adherence to the program. RESULTS: Adherence rates across all ethnic groups were low, 55.5% (229/413) of Han, 55.0% (186/338) of Tibetan, and 47.2% (178/377) of Yi caregivers adhered to the program. Increased adherence was correlated with how each ethnic group received health information. Han caregivers were most influenced by mass media (odds ratio [OR], 1.87; 95% confidence interval [CI], 1.05-3.31), Tibetan caregivers by family (OR, 4.86; 95% CI, 1.45-16.29), and Yi caregivers by village doctors (OR, 6.63; 95% CI, 3.46-12.73). CONCLUSIONS AND IMPLICATIONS: Implementing culturally sensitive health communication strategies will likely improve adherence to home fortification programs among caregivers with distinct ethnic backgrounds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".