Exploring the Link Between Hepatitis A and B Vaccination and Social Support in the US Population: Insights from the NHANES 2005-2006 Cohort
Bibliographic record
Abstract
Background: High social support is easily assessed and associated with a healthier lifestyle, including vaccination adherence. Similarly, immunization is a widely available public health statistic associated with healthier behaviors. However, the link between the hepatitis immunization status and social support needs to be explored. Methods: We assessed the association between levels of social support and hepatitis immunization status using the National Health and Nutrition Examination Survey (NHANES), a cross-sectional multidisciplinary database publicly available for researchers. With 2005–2006 data, 2997 participants were included. Statistical analyses were conducted using STATA18.5v software, including the odds ratio of both univariable logistic regression and multivariable logistic regression. After adjusting for clinically relevant confounders, two distinct models were designed: 1) full immunization, and 2) incomplete immunization. Results: Multivariable analysis revealed significantly greater odds of high social support for participants fully or partially immunized against both hepatitis A and B: 3.15 (95% CI 1.03–9.66,p=0.04) and 3.35 (95% CI 1.15–9.75,p=0.03), respectively. Conclusion: Our findings show an association between vaccination and social support in both adjusted models. Individuals not vaccinated at all may behave differently from those willing to be, at least partially, immunized. It seems that a similar behavior that may lead to vaccination may also lead to increased social support. Therefore, our study suggests that hepatitis vaccination could perhaps be a surrogate marker for public-health-related outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".