HPV vaccination and factors influencing vaccine uptake among people of Indian ancestry living in the United States
Bibliographic record
Abstract
Abstract Approximately one-quarter of annual global cervical cancer deaths occur in India, possibly due to cultural norms promoting vaccine hesitancy. We sought to determine whether people of Indian ancestry (POIA) in the USA exhibit disproportionately lower human papilloma virus (HPV) vaccination rates than the rest of the US population. We utilised the 2018 National Health Interview Survey to compare HPV vaccine initiation and completion rates between POIA and the general US population and determined factors correlating with HPV vaccine uptake among POIA. Compared to other racial groups, POIA had a significantly lower rate of HPV vaccination (8.18% vs. 12.16%, 14.70%, 16.07% and 12.41%, in White, Black, Other Asian and those of other/mixed ancestry, respectively, P = 0.003), but no statistically significant difference in vaccine series completion among those who received at least one injection (3.17% vs. 4.27%, 3.51%, 4.31% and 5.04%, P = 0.465). Among POIA, younger individuals (vs. older), single individuals (vs. married), those with high English proficiency (vs. low English proficiency), those with health insurance and those born in the USA (vs. those born outside the USA) were more likely to obtain HPV vaccination (P = 0.018, P = 0.006, P = 0.029, P = 0.020 and P = 0.019, respectively). Public health measures promoting HPV vaccination among POIA immigrants may substantially improve vaccination rates among this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".