An improved framework to develop Personas applied to willingness of vaccination in the general population

Emanuele Tauro, Alessandra Gorini, Enrico Gianluca Caiani

Keywords: eHealth, Personas, Persuasive System Design, Vaccination, Behavioural Change

TL;DR: We present an improved method for data-driven Personas creation; applying it to a questionnaire and demonstrating its capability of identifying the characteristics of the general population that is willing, or not, to be vaccinated.

Abstract: Vaccine hesitancy is characterized by a multitude of different sociodemographic and psychological factors that require interventions and information to be tailored to the specific users. Thus, the aim of this work is to develop an improved framework to create Personas to identify the characteristics of the population willing to be vaccinated, to facilitate the development of tailored eHealth-based interventions to increase vaccine uptake. Data was collected through an online survey at the beginning of 2021. Multiple dimensionality reduction methods were used to create Personas using K-medoids clustering with PAM algorithm and agglomerative hierarchical clustering with average linkage. The optimal number of Personas and dimensionality reduction methods were chosen through the evaluation of average silhouette graph, total within sum of square distances and percentage of statistically different attributes between clusters. From 1070 respondents, three Personas were identified: one (Persona 3) represented the least willing to be vaccinated compared to the other two (P < 0.001). This information was highly and significantly correlated with lower trust in institutions (P < 0.001), lower level of education (P < 0.001) and lower fear of COVID-19 pandemic (P < 0.001) when compared to the other two Personas. An improved version of a framework to create Personas was applied to identify the characteristics of the population that was less willing to be vaccinated. This approach used a novel indicator, representing the percentage of statistically different attributes among clusters, to identify the optimal number of Personas and the most proper preprocessing methods. Results suggested that tailored interventions should focus on taking advantage of closer social circle of vaccine-hesitant individuals to rebuild trust. This study is the first to use Personas to evaluate willingness of vaccination against the COVID-19 pandemic in the general population to identify potential tailored solutions.

Track: 10. Digital health

Registration Id: VSNP4W6T3SV

Submission Number: 51