April 4-6, 2024 • Hyatt Regency • Lexington, KY
Innovations in Health Communication
Innovations in Health Communication
Abstract: Beyond Definitions: Beginning to Understand the Labeling Effects of “Disparities,” “Inequalities,” and “Inequities”
Scholars often differentiate between health disparities (an existing difference), inequalities (an avoidable and unnecessary difference), and inequities (defining these avoidable, unnecessary differences as unjust and unfair). While clarifying the difference between these terms in research is helpful, they may have little impact on laypersons and their support for policy and understanding of health-related issues. With issue labeling in mind, the current study examines a) what groups these terms bring to mind, b) whether these groups differ by term, and c) whether causal attributions differ by term.
We recruited a representative sample (N = 302) from Prolific. Participants were randomly assigned to answer two open-ended thought-listing exercises and closed-ended questions related to health disparities, inequalities, or inequities. Here, we present results related to group associations and causal attributions. The two authors coded responses to an open-ended question asking which groups came to participants’ minds after reading their assigned term. The two authors determined coding categories from a random sample (n = 30) of responses. All responses were double-coded, with discrepancies resolved through consensus. Causal attributions were examined in terms of whether a) insurance b) the environment c) individual behaviors d) genetics or e) treatment in healthcare were causes for these differences. Logistic regression analyses were conducted on participant agreement or disagreement with causal attributions, controlling for conservatism.
Analysis revealed that the most common group associations were those related to race or ethnicity (55.6%) and socioeconomic status (70.2%). Other group associations included age (29.1%), gender (27.8%), nationality/immigrant status (18.9%) and health status (16.6%), among others. Our analyses revealed no significant differences in group associations or causal attributions based on the term used. Notably, the majority of participants agreed that lack of health insurance (94%; n = 284), the physical environment (83.4%; n = 252), health habits (80.8%; n = 244), genetics (58.6%; n = 177), and differences in treatment (84.4%; n = 255) are possible reasons for health differences. Analyses also revealed conservatism was associated with increased likelihood of agreeing that genetics are the cause of differences (b = .20, p = .005) and decreased likelihood of agreeing that treatment in healthcare is a cause of differences (b = -.25, p = .006).
While we did not find that the terms elicit different group associations or causal attributions, understanding that laypersons may not discern between these terms is useful knowledge for scholars engaged in communication about health equity. First, it raises questions about the importance of educating about discerning between these terms outside the academy. Additionally, these findings could suggest that journalistic reporting practices about health equity issues should change. Secondly, it reveals the types of groups facing differences in health outcomes that seem to be receiving less attention (e.g., LGBTQ; disability). Finally, it underscores the connection between political ideology and views on health equity. Future analyses will examine general associations with the terms, which groups are believed to experience these differential outcomes, and support for policies in order to better understand if these terms impact certain perceptions but not others.
We recruited a representative sample (N = 302) from Prolific. Participants were randomly assigned to answer two open-ended thought-listing exercises and closed-ended questions related to health disparities, inequalities, or inequities. Here, we present results related to group associations and causal attributions. The two authors coded responses to an open-ended question asking which groups came to participants’ minds after reading their assigned term. The two authors determined coding categories from a random sample (n = 30) of responses. All responses were double-coded, with discrepancies resolved through consensus. Causal attributions were examined in terms of whether a) insurance b) the environment c) individual behaviors d) genetics or e) treatment in healthcare were causes for these differences. Logistic regression analyses were conducted on participant agreement or disagreement with causal attributions, controlling for conservatism.
Analysis revealed that the most common group associations were those related to race or ethnicity (55.6%) and socioeconomic status (70.2%). Other group associations included age (29.1%), gender (27.8%), nationality/immigrant status (18.9%) and health status (16.6%), among others. Our analyses revealed no significant differences in group associations or causal attributions based on the term used. Notably, the majority of participants agreed that lack of health insurance (94%; n = 284), the physical environment (83.4%; n = 252), health habits (80.8%; n = 244), genetics (58.6%; n = 177), and differences in treatment (84.4%; n = 255) are possible reasons for health differences. Analyses also revealed conservatism was associated with increased likelihood of agreeing that genetics are the cause of differences (b = .20, p = .005) and decreased likelihood of agreeing that treatment in healthcare is a cause of differences (b = -.25, p = .006).
While we did not find that the terms elicit different group associations or causal attributions, understanding that laypersons may not discern between these terms is useful knowledge for scholars engaged in communication about health equity. First, it raises questions about the importance of educating about discerning between these terms outside the academy. Additionally, these findings could suggest that journalistic reporting practices about health equity issues should change. Secondly, it reveals the types of groups facing differences in health outcomes that seem to be receiving less attention (e.g., LGBTQ; disability). Finally, it underscores the connection between political ideology and views on health equity. Future analyses will examine general associations with the terms, which groups are believed to experience these differential outcomes, and support for policies in order to better understand if these terms impact certain perceptions but not others.