Human papillomavirus vaccine delivery strategies that achieved high coverage in low- and middle-income countries
Bibliographic record
Abstract
OBJECTIVE: To assess human papillomavirus (HPV) vaccination coverage after demonstration projects conducted in India, Peru, Uganda and Viet Nam by PATH and national governments and to explore the reasons for vaccine acceptance or refusal. METHODS: Vaccines were delivered through schools or health centres or in combination with other health interventions, and either monthly or through campaigns at fixed time points. Using a two-stage cluster sample design, the authors selected households in demonstration project areas and interviewed over 7000 parents or guardians of adolescent girls to assess coverage and acceptability. They defined full vaccination as the receipt of all three vaccine doses and used an open-ended question to explore acceptability. FINDINGS: Vaccination coverage in school-based programmes was 82.6% (95% confidence interval, CI: 79.3-85.6) in Peru, 88.9% (95% CI: 84.7-92.4) in 2009 in Uganda and 96.1% (95% CI: 93.0-97.8) in 2009 in Viet Nam. In India, a campaign approach achieved 77.2% (95% CI: 72.4-81.6) to 87.8% (95% CI: 84.3-91.3) coverage, whereas monthly delivery achieved 68.4% (95% CI: 63.4-73.4) to 83.3% (95% CI: 79.3-87.3) coverage. More than two thirds of respondents gave as reasons for accepting the HPV vaccine that: (i) it protects against cervical cancer; (ii) it prevents disease, or (iii) vaccines are good. Refusal was more often driven by programmatic considerations (e.g. school absenteeism) than by opposition to the vaccine. CONCLUSION: High coverage with HPV vaccine among young adolescent girls was achieved through various delivery strategies in the developing countries studied. Reinforcing positive motivators for vaccine acceptance is likely to facilitate uptake.
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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 | 0.004 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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".