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Compliance with COVID-19 safety measures: A test of an objectification theory model.

ABSTRACT: In the present paper, we tested an objectification theory model including compliance with COVID-19 safety measures as an outcome. Safety measures recommended by governments and health organizations include monitoring one's body and interpersonal and social distance from others. We contend that the diffuse safety anxiety stemming from sexual and self-objectification encourages targets to broadly adopt behaviors that protect against body-based dangers, including COVID-19. Accordingly, safety anxiety should predict greater compliance with COVID-19 safety measures. U.S. residents (N = 501) were recruited online and completed measures of sexual objectification, self-objectification, safety anxiety, appearance anxiety, and COVID-19 safety compliance. Two-step mediation analyses revealed a positive indirect effect of sexual objectification on safety anxiety through internalization of observers' perspectives (self-objectification Factor 1); in turn, there was a positive indirect effect of internalized others on COVID-19 body-based safety compliance through safety anxiety. Moreover, women (vs. men) reported higher levels of sexual objectification, internalization of observers' perspectives, safety anxiety, appearance anxiety, and COVID-19 safety measure compliance. Not only is safety anxiety relevant to cautionary behaviors protective against sexual objectification threat, but it also predicts compliance with measures that reduce the risk of contracting COVID-19. Implications for objectification theory are discussed.

SUBMITTER: Earle M

PROVIDER: S-EPMC7985150 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveWe define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB.RationaleThe scientific basis for our inquiry is the received wisdom that infectious disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. If people suffer from EGPB, they will likely make incorrect judgments about their infection risk, which in turn, may lead to reduced compliance of safety protocols.MethodTo collect data on prediction bias, we ran an incentivized, experiment on a global, online platform with participation from people in forty-three countries, each at different stages of progression of COVID-19. We also constructed several indices of compliance by surveying participants about their frequency of hand-washing and use of sanitizers and masks; their willingness to pay for masks; their view about the social appropriateness of others' behavior; and their like/dislike of government responses. The prediction data was used to construct several measures of EGPB. Our experimental design permits us to identify the root of under-prediction as EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold.ResultsRespondents make predictions about the path of the disease using a model that is substantially less convex than the actual data generating process. This creates significant EGPB, which, in turn, is significantly and negatively associated with non-compliance with safety measures. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. A simple behavioral nudge that shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB.ConclusionBehavioral biases concerning the comprehension of disease data are quantitatively important, and act as severe impediments to effective policy action against the spread of COVID-19. Clear communication of future infection risk via raw numbers could increase the accuracy of risk perception, in turn, facilitating compliance with suggested protective behaviors.

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Compliance with COVID-19 safety measures: A test of an objectification theory model.

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