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
Notice bibliographique
Résumé
BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».