Hepatitis B Vaccination Rate in Patients with Diabetes: Assessment of Racial and Socioeconomic Disparity.
Bibliographic record
Abstract
INTRODUCTION: Less hygienic use of blood glucose monitoring equipment such as blood glucose meters, lancets, finger stick devices or other diabetes-care equipment such as syringes or insulin pens by self-administration often exposes the diabetic patient to Hepatitis B infection. This study evaluates hepatitis B vaccination among individuals with diabetes. METHODS: The study used data from the 2000-2013 National Health Interview Survey (NHIS). Vaccination rates among adult individuals with diabetes of various ethnic backgrounds was accessed and compared using chis-square tests. Multivariable logistic regression model was used to compare factors affecting hepatitis B vaccination among individuals with diabetes. RESULTS: The crude rate of diabetes in this population was 5.4%. The rate of vaccination among individuals with diabetes differed across racial groups (Asians 31.8% vs. blacks 30.7%; and whites 26.5%; p<0.01). After multivariate regression, the leading factors affecting hepatitis B vaccination included Age (40-60 years) (OR=0.51, 95% CI=0.47-0.57, p<0.01), lack of college education (OR=0.71,95% CI=0.64-0.79, p<0.01), foreign birth (OR=0.83, 95% CI=0.72-0.95, p<0.01), and Hispanic ethnicity (OR=0.88, 95% CI=0.78-1.00, P<0.05). CONCLUSION: Social and economic factors-education, insurance status, age, poverty level, and place of birth affect rates of vaccination among individuals with diabetes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".