Coping with Non-COVID-19 Health Problems Through Communicative Action in Cyberspace

Abstract

This study investigated how the online health information behaviors of U.S. adults with illnesses unrelated to COVID-19 virus infection affected their coping with health problems and concerns during the COVID-19 pandemic. Guided by the cybercoping model (Kim & Lee, 2014), the study examined associations between these patients’ online information behaviors (information seeking and information forwarding) and coping outcomes (health problems and affective states). The study further explored the mediating roles of health coping processes (problem-and emotion-focused) in the associations between these information behaviors and coping outcomes. Survey data from 687 participants were analyzed using structural equation modeling. The results highlighted the significance of information forwarding in enhancing both coping processes and outcomes, while information seeking enhanced problem-focused coping and health-problem coping outcomes alone. These associations were more pronounced among U.S. adults without chronic conditions than among those with chronic illnesses. These findings’ implications, the study’s limitations, and suggestions for future research were also addressed.

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No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

¹ The situational theory of problem solving (STOPS; J.-N. Kim & Grunig, 2011) is the theoretical foundation of the cybercoping model that defines communication as a potential way to cope with health problems (J.-N. Kim & Lee, 2014). STOPS’ main proposition is that humans are inherent problem solvers, and communication is an instrument that they employ purposefully to resolve problematic life situations (J.-N. Kim & Grunig, 2011). Communication can be specified as various ranges of information behaviors in which people become involved to varying degrees based upon their activeness (active: information-seeking, -forwarding, and -forefending vs. passive: information-attending, -sharing, and -permitting) as their situational motivation in problem solving increases as a function of perceptual and cognitive problem evaluations (Grunig & Kim, 2017; J. N. Kim & Krishna, 2014). Empirical research (e.g., Chon & Park, 2021; H. J. Kim & Hong, 2022; J.-N. Kim et al., 2011) has supported STOPS, which suggests that perceptual and cognitive antecedents have a joint influence on the degree to which people are motivated to solve various health problems (i.e., organ donation, vaccination, outbreaks of infectious diseases), which in turn, increase their various active and passive information behaviors. The cybercoping model specifically uses two active communicative actions proposed by STOPS—information-seeking and -forwarding—as core communicative actions whereby people would engage in cyberspaces to cope with health problems (J.-N. Kim & Lee, 2014). As social actors embedded in relational networks, people with health problems/concerns are highly motivated to adopt problem-solving behaviors and engage simultaneously in seeking and forwarding information from and to other people with health problems/concerns in cyberspaces to achieve successful coping processes and outcomes at individual and collective levels (J.-N. Kim & Lee, 2014).

² The cybercoping model adopts a process-oriented viewpoint on coping, based on the transactional theory of stress and coping proposed by Lazarus (1966) and his colleagues (e.g., Lazarus & Folkman, 1984). According to the transactional theory of stress and coping, people are motivated to allocate ongoing cognitive and behavioral efforts to manage and resolve situations considered taxing, stressful, and overwhelming (Lazarus & Folkman, 1984; Schoenmakers et al., 2015; Wolfers & Schneider, 2021). In response to constant changes in distressing situations, people’s appraisal of the situations and coping strategies undergo dynamic changes over time (Folkman & Lazarus, 1980; Lazarus & Folkman, 1984).

³ Based upon STOPS, which provides the theoretical foundation for the cybercoping model, information forwarding and information sharing are two distinct communicative actions in problem-solving with respect to levels of activeness (J.-N. Kim & Grunig, 2011). Information forwarding is considered a more active form of communication compared to information sharing. The former occurs without solicitation from potential information receivers, while the latter is a reactive response to other information seekers’ specific requests (Grunig & Kim, 2017; J.-N. Kim et al., 2010). In the cybercoping model, individuals with health problems/concerns are presumed to engage in active communicative behavior attributable to their heightened motivational states to resolve the problems that jeopardize their health and well-being (J.-N. Kim & Lee, 2014). Given that information forwarding is a specific sub-type of the active information transmitting behavior dimension that higher problem-solving motivations determine, the cybercoping model focuses on information forwarding as a core communicative action in which individuals with health problems/concerns engage by disseminating their preferred coping strategies and/or experiences to other communicators in cyberspaces.

⁴ In broader contexts, problem-focused coping refers to the process of intending to alter negative and/or stressful situations by taking actions, while emotion-focused coping is directed to regulate emotions, particularly negative ones provoked by stressful situations (Lazarus & Folkman, 1984; Wethington et al., 2015; Wolfers & Schneider, 2021).

⁵ A comprehensive list of various chronic physical illnesses (i.e., diabetes, cardiovascular diseases, cancer, HIV/AIDS) and mental disorders (i.e., post-traumatic stress disorder, schizophrenia) that are prevalent in the U.S. were selected and presented in the survey based upon the information provided on the Centers for Disease Control and Prevention and National Institutes of Health websites. We designed two separate surveys to tailor our informed consents and participant recruitments to meet the specific health problems/concerns associated with each of these two groups of the study participants.

⁶ To prevent situations in which the same participants might respond to two studies posted simultaneously, we scheduled the two studies with slightly different timeframes. We also stated explicitly that individuals who reported any chronic physical and/or mental illnesses at the beginning of the first survey were rejected automatically and excluded from further participation in the second study or compensation, which targeted individuals with chronic illnesses exclusively. After collecting data from both surveys, we compared the unique ID assigned to each MTurk worker across the two studies to identify any instances where the same ID appeared in both sets of studies. This was done to ensure that the same person did not participate in both studies, which targeted different groups of people based upon their current health status.

⁷ Physical illnesses only: n = 111, 32.3%; mental illnesses only: n = 118, 34.3%; both: n = 115, 33.4%.

⁸ The study received the approval of the Institutional Review Board with which the first author was affiliated during data collection period. They received $1.00 in compensation upon completing the surveys.

⁹ Exploratory Factor Analysis (EFA) using a Principal Component Analysis (PCA) with a Varimax rotation was conducted to determine the structure of multiple items associated with each of the key variables. EFA revealed a single factor structure for each of the variables. The eigenvalues and variance that each factor explained were as follows: Information seeking (eigenvalue = 3.86; variance explained = 88.90%); Information forwarding (eigenvalue = 4.31; variance explained = 86.18%); enhancement of problem-focused coping (eigenvalue = 3.30; variance explained = 82.36%); enhancement of emotion-focused coping (eigenvalue = 4.28; variance explained = 71.34%); perceived health-problem coping outcome (eigenvalue = 3.42; variance explained = 85.52%), and perceived affective coping outcomes (eigenvalue = 3.27; variance explained = 81.61%). Prior to testing the research hypotheses, we also confirmed a factor structure that has been examined in the literature (J.-N. Kim & Lee, 2014) to establish the measures’ validity. A confirmatory factor analysis (CFA) with 6 factors yielded a good data-model fit (CFI = .948, TLI = .942, SRMR = .036, RMSEA = .071), which justified further analyses (refer to Table 2 for more details).

¹⁰ We estimated goodness of fit statistics before we tested our hypotheses to ensure the quality of the measurement overall and structural portions of the models (Diamantopoulos et al., 2000). Yuan, Chan, Marcoulides, and Bentler’s refined recommendation for fit indices (2016) was considered together with Hu and Bentler’s joint criteria (1999) to evaluate the model’s fit and test the proposed structural equation. A fair model-data fit would have a CFI (comparative fit index) greater than .92 and RMSEA (root mean square error of approximation) less than .08.

¹¹ Participants’ demographics (age, sex, education, income, race/ethnicity) and offline communication types and frequency were assessed as covariates in our statistical analyses.

¹² Because of some high correlations that could compromise the constructs’ discriminant validity, we conducted additional model comparisons with simpler models. As the correlations between two constructs within information behaviors (information seeking and forwarding), coping processes (enhancement of problem-focused coping and emotion-focused coping), and coping outcomes (health problem coping and affective coping outcomes) were largely high, we compared the 6-factor model to a model with just one general factor that loaded on every item. The Chi-square difference test suggested that the 6-factor model’s added complexity provided a significantly better fit (∆χ² (15) = 3164.6, p < .001). Similarly, we compared the 6-factor model to an alternative 3-factor model where the aforementioned pairs of factors with high correlations were treated as a single factor. The Chi-square difference test did not favor the simpler model and suggested retaining the 6-factor model (∆χ² (15) = 355.7, p < .001), which provided evidence for the construct’s discriminant validity.

¹³ For the multigroup analysis, we compared a series of models to test differences in path coefficients across groups by comparing a restrictive model, in which all parameters are constrained to be equal across groups, with a series of relaxed models. In these relaxed models, each parameter is allowed to vary to estimate whether that single parameter significantly improves model fit when allowed to vary between groups. To examine the basic cybercoping model, where communicative actions (i.e., information seeking and forwarding) affect coping outcomes (i.e., health-problem coping and affective coping), we compared the restrictive model where all parameters are equal between two groups, with relaxed models, where the two regression coefficients with the same outcome variables are allowed to vary between the groups.

¹⁴ Examining the group differences in the mediation effects follows the same principle. A series of model comparisons between the restricted model (the model with equal constraints for all parameters) and relaxed models (models with relaxed constraints) were conducted. Testing the difference in mediation effect requires freeing six parameters, including two regression coefficients for a mediator on two predictors, and four regression coefficients for an outcome on two mediators and two predictors. Since a part of indirect effects requires the same configuration of free parameters, we only need to test four configurations of free parameters against the restricted model to examine all eight indirect effects.

¹⁵ Standardized factor loading..

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Funding

The author(s) reported there is no funding associated with the work featured in this article.