Initial Coverage and Regional Disparities of the National HPV Vaccination Program in Poland: A Cross-Sectional Analysis
Bibliographic record
Abstract
Background/Objectives: Cervical cancer is the second most common gynecological cancer worldwide, preventable through screening initiatives and vaccinations against its causative agent, anogenital human papillomavirus (HPV). This study aimed at measuring the coverage and uptake of the national HPV vaccination program launched in 2023 and implemented throughout Poland. Methods: This cross-sectional, observational study analyzed population data of adolescents in 11–13-year-old groups vaccinated in individual voivodeships (provinces) of Poland as provided by the National Health Fund and the Central Statistical Office. A p-value of <0.05 was considered statistically significant. Results: The rate of HPV vaccination participation under the population program was 8.67%. In the analyzed age groups, in both sexes, no statistically significant correlation was observed between the population size at a given age and population coverage or participation in HPV vaccination. However, a positive relationship in vaccination coverage was observed in individuals previously vaccinated with one dose in subsequent age groups, indicating a continued willingness to receive vaccination with further doses. No statistically significant difference in population coverage changes across voivodeships was found between the number of doses within the urban population share vs. rural population share. Conclusions: Our results show that, at 1.5 years of implementation of the national HPV vaccination program, the coverage and uptake of the program is considerably insufficient. The intensive corrective actions indicated are required to pave this program forward towards optimum results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".