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"The work is always accomplished one word at a time"

Stephen King

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A Quantitative Article Critique:

Critiquing the article:

Mental health during the COVID-19 pandemic: Effects of stay-at-home policies, social distancing behavior, and social resources

Introduction

In the article “Mental health during the COVID-19 pandemic: Effects of stay-at-home policies, social distancing behavior, and social resources”, Brett Marroquína, Vera Vineb, and Reed Morgana (2020) have shown important findings showing that these implemented social distancing practices may have specific negative impacts on people’s  mental health. In this paper we will review and provide analysis with the observations looking at the nature of this study, its objectives and how well they are achieved, how the design was evaluated, and the appeal of the study.

Summary

Marroquín (2020) conducted a series of exploratory analyses to examine potential factors that might have been associated with stay-at-home orders to a person’s behavior. Researchers provided informed consent to the sample of 435 participants and collected information from them completing a section of questionnaires examining whether the distancing effects, considering the pre-existing mental health conditions of a participant have showed negative impacts on their mental health. The researchers focused on sensitive outcomes such as depression, generalized anxiety disorder, intrusive thoughts, insomnia, and acute stress. In the end, results indicate that distancing was at least associated with worsening mental health course during this period.

Method

Following the introduction, the researchers stated the procedures, context, participants, measures, and the analytic plan of the study. The article focused on two (2) time points of an ongoing multi-wave analysis in the national online sample of adults (Marroquín, et al., 2020, p. 2). The first point was during a 3-day period (the “March 2020” wave) from March 26 to March 28, 2020, which gathered 435 samples. The second point was collected from February 18 to March 1, 2020 (the “February 2020” wave), where 118 of these sample also participated in the following “March 2020” wave. We believe that the set of samples collected is inconsistent, due to the inadequate timeframe used for the collection of data. It would have been better if the authors used a chronological timeframe to have a precise data on a specific wave or period. Subsequently, the recruitment was limited to adults living in the United States. Participants were initially selected and verified through Amazon’s Mechanical Turk (MTurk) system and were led to the Qualtrics survey (Marroquín, et al., 2020, p. 2). We assume that the selected samples helped the authors to find more reliable data upon the study.

After the data collections, the authors then described the independent and the dependent variables. The independent variables were the participant’s age, gender, education, and income. It was done through the health measure scales conducted for depressive symptoms (February and March), generalized anxiety disorder symptoms (February and March), intrusive thoughts (March), insomnia (March), and acute stress (March). As well as the social resource measures of the social support and the social network size (Marroquín, et al., 2020, pp. 2-3). On the other hand, the dependent variable was the mental health effects experienced by the participants. Thus, such measures administered were not sufficient enough to provide general outcome of the supposed effects, because they only pointed out particular periods of time and were administered only to limited participants.

Moreover, the researchers did not include any confounding variables of the study, which might result to internal validity threat. It would have been helpful if this study included the environment of each participant, as well as the previous or other related mental health issues experienced before and during the pandemic. This could help build a stronger data for accurate outcomes. Furthermore, researchers conducted preliminary analyses to describe the sample, which included prevalence of clinically significant symptoms, and to examine relevant bivariate relationships, that consequently established useful informations on how the participants would respond, as well as on how the researchers will approach it. In these analyses they formulated three main phases (Marroquín, et al., 2020, pp. 3-4):

1. To examine cross-sectional hypotheses regarding social distancing correlates of mental health symptoms were entered into a series of multiple regression analyses.

2. The hypothesis of depression and the symptoms of GAD in the population increased from late February to late March with regular measurement tests of symptoms in the prospective subsample.

3. The hypothesis that social distancing would predict the symptoms of March, over and beyond social resources, and the symptoms of February, was tested in multiple regression analyses similar to those conducted cross-sectionally in the entire sample, however with constant hold of February symptoms.

Results

At the end of methods, the researchers had used different methods for the outcome measures to be evaluated. Descriptive statistics were used to measure prevalence of clinically-relevant symptoms which had provided the brief summary of the samples and the measures done, Zero-order associations among study variables helped in examining the relationship of the findings of the variables in the study. The researchers used ANOVA tests for the analysis of data.  The measures found was then presented in tables (Marroquín, et al., 2020, pp. 4-5). With these methods conducted in the study, they have properly evaluated and judged the overall findings of the data which then helped in creating hypotheses upon the acquired results. The hypotheses are as follows:

1. Social distancing activity would be associated with increased symptoms cross-sectionally, above and beyond the optimized effects of social support and social network size.

2. Mental health symptoms are expected to increase from February to March.

3. Severity of possible symptom increases will be correlated with the degree of social distance, regardless of the social resources.

Discussion, Limitations, Conclusions, and Recommendations

After the results, the authors concentrated on the findings collected, that reached distinct impacts as they have examined the mental health effects associated with social distancing. The authors found evidence on how both government stay-at-home rules and personal social distance actions were correlated with symptoms of a variety of mental health conditions (Marroquín, et al., 2020, p. 6). These findings are mirrored by early cross-sectional evidence that COVID-19 pandemic may affect mental health in general population (Cao et al., 2020; Tull et al., 2020) and can extend to a wider range of mental health effects. Hence, the results clearly explained that certain health protocols can cause mild to severe syptoms of different mental health issues. At the close end of the discussion, the researchers put into account the study's limitations, such as the causal direction that could affect personal distancing behavior, the plausible confounding variables, the range of pandemic-related experiences, the mechanistic psychological processes, the moderators of distancing effect, and the longitudinal work (Marroquín, et al., 2020, p. 7). These limitations can serve as a guide in future studies as the pandemic unfolds. The study also suggest that it should not be interpreted as a generalized sample or nationally representative. Therefore, this matter might cause validity conflicts.

Near the end of the article, the researchers voiced out the strengths of the study, as well as the recommendation. First, the national adult sample was used to be reasonably representative of the United States, primarily in terms of age, gender, education and income. Second, the investigation of two (2) time points that captured the phenomenal period of initial adjustment to COVID-19. Third, the inclusion of social support and social network size stated the relevance social distancing in the context of other social factors involved in mental health. Fourth, the well-tested measures of specific mental health outcomes have been used, and the sample has represented a wide range of most types of symptoms. In addition, the study recommended that the novel approach to determining the level of social distancing activity appears to be psychometrically sound based on this initial study and may be promising for future COVID-19 studies (Marroquín, et al., 2020, p. 7).

This article provides a reliable and an acceptable information on how certain global health crisis, like the COVID-19 can correlate to our mental health. It proves that such issues must be taken proper deliberation and response in order to prevent further consequences. Though this study should not be represented in the general population, it will still be beneficial for future studies in the psychiatry, society, and global health fields.

References

Morroquina, B., Vine, V., & Morgana, R. (2020). Mental health during the COVID-19 pandemic: Effects of stay-at-home policies, social distancing behavior, and social resources. Psychiatry Research, 293, 1-9. Retrieved from https://www.researchgate.net/publication/343783896_Mental_Health_During_the_COVID-19_Pandemic_Effects_of_Stay-at-Home_Policies_Social_Distancing_Behavior_and_Social_Resources

Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research, 287, 112934. Retrieved from https://doi.org/10.1016/j.psychres.2020.112934.

Tull, M.T., Edmonds, K.A., Scamaldo, K.M., Richmond, J.R., Rose, J.P., Gratz, K.L. (2020). Psychological outcomes associated with stay-at-home orders and the perceived impact of COVID-19 on daily life. Psychiatry Research, 289, 113098. Retrieved from https://doi.org/10.1016/j.psychres.2020.113098.

Authors: Karylle Miralles & Brigid Gonzaga