The study examined whether the 3R (reframe, prioritize, and reform) communication model intervention can impact parents’ and adolescents’ HPV vaccination acceptability. We used face-to-face methods to recruit participants from three local churches in the Ashanti Region of Ghana. Participants completed pre- and post-intervention assessments based on the validated Theory of Planned Behavior survey. We organized two face-to-face presentations for parents and adolescents separately for parents (n = 85) and adolescents (n = 85). Participants’ post-intervention vs. pre-intervention scores for attitude (mean = 35.46 ± SD = 5.46 vs. mean = 23.42 ± SD = 8.63), knowledge (M = 28.48 ± SD = 5.14 vs. M = 16.56 ± SD = 7.19), confidence (M = 8.96 ± SD = 3.43 vs. M = 6.17 ± SD = 2.84), and intention for vaccine acceptance (M = 4.73 ± SD = 1.78 vs. M = 3.29 ± SD = 1.87) increased significantly (p < 0.001). The intervention showed that for every one-unit increase in the participants’ self-confidence and attitude scores, the odds of the HPV vaccination acceptability increased by 22% (95% CI: 10–36) and 6% (95% CI: 0.1–12), respectively. Intention for vaccine acceptance, F (1167) = 6.89, and attitude toward vaccination, F (1167) = 19.87, were significantly higher among parents than adolescents (p < 0.001), after controlling for the baseline scores. These findings suggest that the intervention targeting parents’ and adolescents’ attitudes and knowledge has the potential to increase HPV vaccination acceptance in Ghana.

Merging spatial data from different networks can offer several advantages, including accurate prediction and reduced uncertainty. However, data are often collected in different ways, resulting in heterogeneity among networks. Data Harmonization is a statistical approach for merging diverse datasets to ensure compatibility and consistency. In the last decades, vast and complex datasets have been frequently generated. These datasets are typically too large and dynamic to be effectively managed and analyzed using traditional methods. Symbolic data analysis has recently been employed to simplify analysis and summarize information from data instead of using large data sets while retaining as much information as possible. In this study, we propose a harmonized kriging for spatial interval-valued data to perform efficient and accurate prediction for large volumes of spatial data. A simulation study illustrates the superiority of the proposed method as compared to kriging with pooled or referenced data. We demonstrate the application of harmonized kriging using interval-valued temperature data from three distinct monitoring networks in South Korea.