What do people really think about the RSV vaccine? Study of unsolicited text replies from adults over 60
Bibliographic Data
| ID | 22233455 |
|---|---|
| Authors | E Susanne Blazek (0000-0003-1649-3252), Sarah Deedat (0009-0006-4145-7279), Olivia Kinney (0000-0002-3063-6523), Allison M Londerée (0000-0001-9641-2058), Stacey Frede (0009-0000-7109-4057), Amy Bucher (0000-0001-6514-4441, corresponding author) |
| Year | 2025 |
| Volume | 12 |
| Issue | 1 |
| Publication date | 2025-05-11 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Humanities and Social Sciences Communications (JOURNAL) |
| Journal identifiers | ISSN: 2662-9992 • E-ISSN: 2662-9992 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1057/s41599-025-04961-2 |
| OpenAlex | W4410256153 |
| Language | EN |
| References cited | 81 |
A digital health intervention (DHI) using SMS precision nudging to drive RSV vaccine uptake among adults over 60 was launched with a large community pharmacy chain in 2023, two months after the vaccine’s FDA approval for adult administration in the United States. Tens of thousands of patients texted back. In this exploratory investigation, we employed thematic analysis and structural topic modelling to extract topics (e.g., Kind declines, Moral disgust, etc.), sentiment (negative to positive), and function (emotional to practical) expressed in the text replies in order to understand patient attitudes and behavioural determinants of RSV vaccination. The analyses reveal 10 topics from the thematic analysis and 30 more granular topics from the structural topic modelling. Expressed attitudes shifted over the course of the DHI, with less negativity later in the intervention. People who did not receive the flu vaccine and people with commercial insurance responded more frequently that they would not get vaccinated. Specific behaviour change techniques (BCTs) were associated with overall replies and specific topics. Framing intervention messages with information about emotional consequences elicited the highest proportion of replies; framing with anticipated regret elicited the lowest proportion. We extend previous work by leveraging unsolicited replies to analyse public attitudes toward a new vaccine, with implications for future RSV and other vaccine interventions at pharmacies and elsewhere
Family medicine · Hate Speech and Cyberbullying Detection · Medicine · Misinformation and Its Impacts · Psychology · Vaccine Coverage and Hesitancy · Virology
Grounded theory in practice
Human-level control through deep reinforcement learning
Antecedents to Psychological Reactance
Characteristics of Qualitative Descriptive Studies
Social media and attitudes towards a Covid-19 vaccination
Qualitative methods in implementation research
Quanteda
Anticipated regret and health behavior
Understanding vaccine hesitancy around vaccines and vaccination from a global perspective
The behaviour change wheel
Applying machine-learning to rapidly analyze large qualitative text datasets to inform the Covid-19 pandemic response
Sentiment in Twitter events
Rural‐urban and within‐rural differences in Covid‐19 vaccination rates
Changes in Short-term, Long-term, and Preventive Care Delivery in US Office-Based and Telemedicine Visits During the Covid-19 Pandemic
Attitudes, behaviours and barriers to public health measures for Covid-19
Analysing user-reported data for enhancement of SmokefreeTXT
Overestimation of Covid-19 Vaccination Coverage in Population Surveys Due to Social Desirability Bias
Using thematic analysis in psychology
Structural Topic Models for Open‐Ended Survey Responses
The impact of social desirability bias on conspiracy belief measurement across cultures
The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques
Global evidence of expressed sentiment alterations during the Covid-19 pandemic
Conspiracy theories as barriers to controlling the spread of Covid-19 in the U.S
| Highly cited | No |
|---|