# Motivators and Demotivators for COVID-19 Vaccination Based on Co-Occurrence Networks of Verbal Reasons for Vaccination Acceptance and Resistance: Repetitive Cross-Sectional Surveys and Network Analysis

**Authors:** Qiuyan Liao, Jiehu Yuan, Irene Oi Ling Wong, Michael Yuxuan Ni, Benjamin John Cowling, Wendy Wing Tak Lam

PMC · DOI: 10.2196/50958 · JMIR Public Health and Surveillance · 2024-04-22

## TL;DR

This study identifies key motivators and demotivators for COVID-19 vaccination in Hong Kong using network analysis of survey responses.

## Contribution

The study introduces a novel approach using co-occurrence networks to assess the relative importance of verbal reasons for vaccine acceptance and resistance.

## Key findings

- Perceived personal risk and social responsibility were top motivators for vaccination.
- Lack of vaccine confidence and complacency were top demotivators for vaccination.
- Age-specific differences were found in motivators and demotivators for vaccination.

## Abstract

Vaccine hesitancy is complex and multifaced. People may accept or reject a vaccine due to multiple and interconnected reasons, with some reasons being more salient in influencing vaccine acceptance or resistance and hence the most important intervention targets for addressing vaccine hesitancy.

This study was aimed at assessing the connections and relative importance of motivators and demotivators for COVID-19 vaccination in Hong Kong based on co-occurrence networks of verbal reasons for vaccination acceptance and resistance from repetitive cross-sectional surveys.

We conducted a series of random digit dialing telephone surveys to examine COVID-19 vaccine hesitancy among general Hong Kong adults between March 2021 and July 2022. A total of 5559 and 982 participants provided verbal reasons for accepting and resisting (rejecting or hesitating) a COVID-19 vaccine, respectively. The verbal reasons were initially coded to generate categories of motivators and demotivators for COVID-19 vaccination using a bottom-up approach. Then, all the generated codes were mapped onto the 5C model of vaccine hesitancy. On the basis of the identified reasons, we conducted a co-occurrence network analysis to understand how motivating or demotivating reasons were comentioned to shape people’s vaccination decisions. Each reason’s eigenvector centrality was calculated to quantify their relative importance in the network. Analyses were also stratified by age group.

The co-occurrence network analysis found that the perception of personal risk to the disease (egicentrality=0.80) and the social responsibility to protect others (egicentrality=0.58) were the most important comentioned reasons that motivate COVID-19 vaccination, while lack of vaccine confidence (egicentrality=0.89) and complacency (perceived low disease risk and low importance of vaccination; egicentrality=0.45) were the most important comentioned reasons that demotivate COVID-19 vaccination. For older people aged ≥65 years, protecting others was a more important motivator (egicentrality=0.57), while the concern about poor health status was a more important demotivator (egicentrality=0.42); for young people aged 18 to 24 years, recovering life normalcy (egicentrality=0.20) and vaccine mandates (egicentrality=0.26) were the more important motivators, while complacency (egicentrality=0.77) was a more important demotivator for COVID-19 vaccination uptake.

When disease risk is perceived to be high, promoting social responsibility to protect others is more important for boosting vaccination acceptance. However, when disease risk is perceived to be low and complacency exists, fostering confidence in vaccines to address vaccine hesitancy becomes more important. Interventions for promoting vaccination acceptance and reducing vaccine hesitancy should be tailored by age.

## Linked entities

- **Diseases:** COVID-19 (MONDO:0100096)

## Full-text entities

- **Diseases:** COVID-19 (MESH:D000086382), Vaccine (MESH:D004673)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

66 references — full list in the complete paper: https://tomesphere.com/paper/PMC11074890/full.md

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Source: https://tomesphere.com/paper/PMC11074890