April 4-6, 2024 • Hyatt Regency • Lexington, KY
Innovations in Health Communication
Innovations in Health Communication
Abstract: Understanding Cancer Screening Intentions as a Direct and Mediated Response to Information Seeking Through the S-O-R Framework
The Stimulus-Organism-Response Framework was introduced by Mehrabian & Russell (1974) to inform the study of Environmental Psychology. The SOR framework posits that people cognitively and affectively process (i.e., organism, O) objects or information in their environments (i.e., stimulus, S) and react to it, either through approach or avoidance behaviors (i.e., response, R). This theory has been applied to multiple scholarly domains and several studies have added nuance to the SOR framework specifically in the context of health information seeking. This paper seeks to extend this line of research by using the SOR framework to explain cancer screening intention as a direct and psychologically mediated response to online cancer information seeking.
H1: Cancer information seeking (S) will be positively associated with cancer screening intentions (R).
Yang and LaValley (2016) suggest that health information seeking research should employ more diverse measurement topics such as barriers and motivations for information seeking. In a similar vein, recent works have demonstrated the relevance of negative emotions about COVID-19 (e.g., emotional distress, Hwang et al., 2021; worry, Liu, 2020) in the information seeking and preventative behavior literature. In this spirit, this paper explores the possibility of information search self-efficacy, excessive effort needed to get information, and information overload as organism factors (i.e., mediators) of the relationship between cancer information seeking and screening intentions.
RQ1: Does information search self-efficacy, a lot of effort needed to get information, and information overload (O) mediate the relationship between cancer information seeking (S) and cancer screening intentions (R)?
This study is a secondary analysis of Health Information National Trends Survey (HINTS) data from National Cancer Institute (NIH). Since 2003, HINTS has regularly collected data about the American public’s knowledge of, attitudes toward, and use of cancer and health information. Among publicly available data, this study used the most recent administration, HINTS 6, released for public access in April 2023. This was conducted from March 7 – November 8, 2022. The final HINTS 6 sample consisted of 6,252 respondents. Results of a preliminary exploration of the data (unweighted) through a comparison of two models is as follows. Hierarchical regression results comparing Model 1 (cancer information seeking, effort during information seeking, information self-efficacy, information overload as predictors of interest and having had cancer, age, birth gender, education, and race as covariates) and Model 2 (cancer information seeking, effort during information seeking, information self-efficacy, information overload, perceived self-efficacy of taking care of one’s own health, concern about cancer information quality, and information being difficult to understand as predictors of interest, with the same covariates as the first model) indicated a significant increase in predictive power, F-change (3, 6239) = 61.599, p < .001. Therefore, it was included that the addition of the three predictors improved model fit. In amalgamation with regression results from model 2, it could be tentatively concluded that one’s own ability to take care of health was a predictor worthy of adding to the research model for the main analysis – which will include structural equation modeling (SEM).
H1: Cancer information seeking (S) will be positively associated with cancer screening intentions (R).
Yang and LaValley (2016) suggest that health information seeking research should employ more diverse measurement topics such as barriers and motivations for information seeking. In a similar vein, recent works have demonstrated the relevance of negative emotions about COVID-19 (e.g., emotional distress, Hwang et al., 2021; worry, Liu, 2020) in the information seeking and preventative behavior literature. In this spirit, this paper explores the possibility of information search self-efficacy, excessive effort needed to get information, and information overload as organism factors (i.e., mediators) of the relationship between cancer information seeking and screening intentions.
RQ1: Does information search self-efficacy, a lot of effort needed to get information, and information overload (O) mediate the relationship between cancer information seeking (S) and cancer screening intentions (R)?
This study is a secondary analysis of Health Information National Trends Survey (HINTS) data from National Cancer Institute (NIH). Since 2003, HINTS has regularly collected data about the American public’s knowledge of, attitudes toward, and use of cancer and health information. Among publicly available data, this study used the most recent administration, HINTS 6, released for public access in April 2023. This was conducted from March 7 – November 8, 2022. The final HINTS 6 sample consisted of 6,252 respondents. Results of a preliminary exploration of the data (unweighted) through a comparison of two models is as follows. Hierarchical regression results comparing Model 1 (cancer information seeking, effort during information seeking, information self-efficacy, information overload as predictors of interest and having had cancer, age, birth gender, education, and race as covariates) and Model 2 (cancer information seeking, effort during information seeking, information self-efficacy, information overload, perceived self-efficacy of taking care of one’s own health, concern about cancer information quality, and information being difficult to understand as predictors of interest, with the same covariates as the first model) indicated a significant increase in predictive power, F-change (3, 6239) = 61.599, p < .001. Therefore, it was included that the addition of the three predictors improved model fit. In amalgamation with regression results from model 2, it could be tentatively concluded that one’s own ability to take care of health was a predictor worthy of adding to the research model for the main analysis – which will include structural equation modeling (SEM).