The impact of school-entry mandates on social inequalities in human papillomavirus vaccination
Bibliographic record
Abstract
Fundamental cause theory (FCT) is influential for explaining the enduring relationship between social position and health, yet few empirical studies test FCT’s contention that policy supporting the equal distribution of interventions across populations can help reduce health inequalities. Following human papillomavirus (HPV) vaccine approval, complex socioeconomic and racial-ethnic inequalities emerged in distinct stages of the diffusion of this health innovation. Virginia and the District of Columbia were the first U.S. jurisdictions to implement school-entry HPV vaccination mandates for sixth-grade girls, offering an opportunity to test whether inequalities in HPV vaccination are mitigated by policy that seeks to standardize the age of vaccine administration and remove barriers to knowledge about the vaccine. Using data from the 2008, 2009, 2011, 2012, and 2013 National Immunization Survey–Teen (N = 4579) and a triple-difference approach, this study tests whether vaccine mandates are associated with smaller socioeconomic and racial-ethnic inequalities in health provider recommendation and vaccine uptake. It finds mandates were associated with improvements in provider recommendation and vaccine uptake for some socioeconomic and racial-ethnic groups. However, mandates also likely led to a decline in HPV vaccine series completion overall. Implications of these findings for informing FCT and vaccination policy are discussed.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".