Eder Stephanie J, Nicholson Andrew A, Stefanczyk Michal M, Pieniak Michał, Martínez-Molina Judit, Pešout Ondra, Binter Jakub, Smela Patrick, Scharnowski Frank, Steyrl David
Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria.
Institute of Psychology, University of Wrocław, Wrocław, Poland.
Front Psychol. 2021 Jul 21;12:647956. doi: 10.3389/fpsyg.2021.647956. eCollection 2021.
The COVID-19 pandemic along with the restrictions that were introduced within Europe starting in spring 2020 allows for the identification of predictors for relationship quality during unstable and stressful times. The present study began as strict measures were enforced in response to the rising spread of the COVID-19 virus within Austria, Poland, Spain and Czech Republic. Here, we investigated quality of romantic relationships among 313 participants as movement restrictions were implemented and subsequently phased out cross-nationally. Participants completed self-report questionnaires over a period of 7 weeks, where we predicted relationship quality and change in relationship quality using machine learning models that included a variety of potential predictors related to psychological, demographic and environmental variables. On average, our machine learning models predicted 29% (linear models) and 22% (non-linear models) of the variance with regard to relationship quality. Here, the most important predictors consisted of attachment style (anxious attachment being more influential than avoidant), age, and number of conflicts within the relationship. Interestingly, environmental factors such as the local severity of the pandemic did not exert a measurable influence with respect to predicting relationship quality. As opposed to overall relationship quality, the change in relationship quality during lockdown restrictions could not be predicted accurately by our machine learning models when utilizing our selected features. In conclusion, we demonstrate cross-culturally that attachment security is a major predictor of relationship quality during COVID-19 lockdown restrictions, whereas fear, pathogenic threat, sexual behavior, and the severity of governmental regulations did not significantly influence the accuracy of prediction.
2020年春季起在欧洲实施的新冠疫情及相关限制措施,有助于识别不稳定和压力时期关系质量的预测因素。本研究始于奥地利、波兰、西班牙和捷克共和国针对新冠病毒传播加剧而实施严格措施之时。在此,我们调查了313名参与者的浪漫关系质量,当时跨国实施并随后逐步取消了行动限制。参与者在7周内完成了自我报告问卷,我们使用机器学习模型预测关系质量和关系质量的变化,这些模型包括各种与心理、人口和环境变量相关的潜在预测因素。平均而言,我们的机器学习模型预测了关系质量方面29%(线性模型)和22%(非线性模型)的方差。其中,最重要的预测因素包括依恋风格(焦虑依恋比回避依恋更具影响力)、年龄和关系中的冲突数量。有趣的是,诸如当地疫情严重程度等环境因素在预测关系质量方面没有产生可测量的影响。与整体关系质量不同,在实施封锁限制期间关系质量的变化,在我们使用选定特征时,无法通过我们的机器学习模型准确预测。总之,我们通过跨文化研究表明,在新冠疫情封锁限制期间,依恋安全性是关系质量的主要预测因素,而恐惧、致病威胁、性行为和政府规定的严格程度并未显著影响预测的准确性。