Association between maternal health literacy and child vaccination in India: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Education of mothers may improve child health. We investigated whether maternal health literacy, a rapidly modifiable factor related to mother's education, was associated with children's receipt of vaccines in two underserved Indian communities. METHODS: Cross-sectional surveys in an urban and a rural site. We assessed health literacy using Indian child health promotion materials. The outcome was receipt of three doses of diphtheria-tetanus-pertussis (DTP3) vaccine. We used multivariate logistic regression to investigate the relationship between maternal health literacy and vaccination status independently in each site. For both sites, adjusted models considered maternal age, maternal and paternal education, child sex, birth order, household religion and wealth quintile. Rural analyses used multilevel models adjusted for service delivery characteristics. Urban analyses represented cluster characteristics through fixed effects. RESULTS: The rural analysis included 1170 women from 60 villages. The urban analysis included 670 women from nine slum clusters. In each site, crude and adjusted models revealed a positive association between maternal health literacy and DTP3. In the rural site, the adjusted OR was 1.57 (95% CI 1.11 to 2.21, p=0.010) for those with medium health literacy, and OR=1.30 (95% CI 0.89 to 1.91, p=0.172) for those with high health literacy. In the urban site, the adjusted OR was 1.10 (95% CI 0.65 to 1.88, p=0.705) for those with medium health literacy, and OR=2.06 (95% CI 1.06 to 3.99, p=0.032) for those with high health literacy. CONCLUSIONS: In these study settings, maternal health literacy is independently associated with child vaccination. Initiatives targeting health literacy could improve vaccination coverage.
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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 | 0.144 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".