The effort to limit the spread of the coronavirus (COVID-19) has relied heavily on the general public's compliance with health guidelines limiting social contact and mitigating risk when contact occurs. The aim of this study was to identify latent variables underlying adherence to COVID-19 guidelines and to examine demographic and psychological predictors of adherence. A sample of US adults (N = 1,200) were surveyed in late April to mid-May 2020. The factor structure of adherence was examined using exploratory factor analysis. Machine learning regression models using elastic net regularization were used to examine predictors of adherence. Two factors characterized adherence: avoidance and cleaning. Elastic net models identified differential demographic and psychological predictors of these two forms of adherence. Religious affiliation, denial coping, full-time employment, substance use coping, and being 60 or older predicted lower avoidance adherence. Behavioral and mindfulness emotion regulation skills, agreeableness, and Democrat political affiliation predicted greater avoidance adherence. For cleaning adherence, interpersonal and behavioral emotion regulation skills and conscientiousness emerged as strong predictors of greater cleaning. Efforts to promote compliance with COVID-19 health guidelines may benefit from distinguishing avoidance and cleaning adherence and considering predictors of each of these aspects of adherence.

中文翻译：

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遵守 COVID-19 健康指南：检查遵守情况的人口和心理预测因素


限制冠状病毒（COVID-19）传播的努力在很大程度上依赖于公众遵守限制社交接触和减轻接触风险的健康指南。本研究的目的是确定遵守 COVID-19 指南的潜在变量，并检查遵守情况的人口和心理预测因素。 2020 年 4 月下旬至 5 月中旬对美国成年人样本（ N = 1,200）进行了调查。使用探索性因素分析检查了依从性的因素结构。使用弹性网络正则化的机器学习回归模型用于检查依从性的预测因子。依从性的特征有两个因素：回避和清洁。弹性网络模型确定了这两种依从形式的差异人口和心理预测因素。宗教信仰、否认应对方式、全职工作、物质使用应对方式以及年龄在 60 岁或以上预示着回避依从性较低。行为和正念情绪调节技能、宜人性和民主党政治立场预示着更大的回避依从性。对于清洁依从性而言，人际和行为情绪调节技能以及责任心成为加强清洁的有力预测因素。区分回避和清洁依从性并考虑依从性各个方面的预测因素可能会有益于促进遵守 COVID-19 健康指南。