First Advisor

Randy Larkins

First Committee Member

Maria Lahman

Second Committee Member

Chia-Lin Tsai

Third Committee Member

Dannon Cox

Degree Name

Doctor of Philosophy

Document Type

Dissertation

Date Created

12-2024

Department

College of Education and Behavioral Sciences, Applied Statistics and Research Methods, ASRM Student Work

Abstract

The purpose of this study is to gain an understanding of parental attitudes toward childhood vaccinations and decision-making styles. Overall, this study worked to comprehensively examine the factors influencing vaccine hesitancy among parents of children under 18 utilizing quantitative and qualitative methodologies and conducting qualitative analytic comparisons to enhance understanding for future research practices.

The study’s objective in the first phase was to quantitatively assess whether decision-making styles predict vaccine hesitancy, considering demographic factors. The contextual research question guiding this phase was, “Do general decision-making styles predict vaccine hesitancy controlling for age, gender, level of education, employment status, marital status, social class, ethnicity, geographic location, and number of children? ”A survey of 260 parents revealed that those with an intuitive decision-making style had 86% lower odds of being vaccine-hesitant, while parents with three or more children had 73% lower odds of exhibiting vaccine hesitancy. However, the sample’s homogeneity limits the generalizability of these findings. The second phase entailed qualitatively exploring the underlying parental perspectives contributing to vaccine hesitancy. The contextual research question guiding this phase was, “How do parents’ underlying beliefs contribute to vaccine hesitancy?” The qualitative phase, involving 17 semi-structured interviews with 18 participants, was analyzed using both traditional reflexive thematic analysis (RTA) with NVivo and RTA enhanced by artificial intelligence (AI) with ChatGPT. The methodological research question was, “What are the distinctions between reflexive thematic analysis using ChatGPT and traditional analysis using NVivo?” The study found that traditional RTA provided deeper and more comprehensive insights, consistent coding, in-depth participant quotes, and a rigorous iterative process, making it more effective in addressing the research questions. While AI-driven RTAs were efficient and satisfactory in identifying themes, they lacked the depth of traditional RTA. However, compared to the significant time and expenses associated with traditional RTA using NVivo, AI's capacity to perform analyses within minutes and its cost-efficiency underscores its potential as a valuable co-researcher, especially for those working with limited resources. This study highlights the importance of thoroughly exploring vaccine hesitancy, decision-making styles, and underlying beliefs of parents while also contributing to the evolving discourse on AI’s role in qualitative research.

Abstract Format

html

Keywords

AI-assisted analysis; decision-making styles; public health communication; vaccine hesitancy; reflexive thematic analysis

Language

English

Extent

347 pages

Local Identifiers

Brown_unco_0161D_11290

Rights Statement

Copyright is held by the author.

Digital Origin

Born digital

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