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Social Infrastructure and the Alleviation of Loneliness in Europe.

Christopher S SwaderAndreea-Valentina Moraru
Published in: Kolner Zeitschrift fur Soziologie und Sozialpsychologie (2023)
In Europe, individualist societies, in which people more highly value independence, have fewer people who are lonely. Yet these societies also have more people who live alone, a strong determinant of loneliness. Evidence suggests that some unrecognized societal-level resources or characteristics can explain this. We uncover multiple pathways toward a lower degree of loneliness among European societies using an ideal method for this purpose, fuzzy-set qualitative comparative analysis. Using data from the 2014 wave of the European Social Survey and other sources, we analyzed loneliness outcomes among 26 European societies. Our findings suggest two necessary conditions for a low degree of loneliness: high internet access and high association participation. Further, three pathways are sufficient for achieving less loneliness at the societal level. Most societies that have less loneliness follow both the welfare support and cultural support pathways. The third path, commercial provision, is mutually exclusive with welfare support because the former requires a weak welfare state. The surest policy for building societies that have lower rates of loneliness includes the expansion of internet accessibility, the fostering of civil society through association participation and volunteering, and a welfare state that protects potentially vulnerable populations while funding opportunities for social interaction. This article further contributes methodologically by demonstrating "configurational robustness testing," a more comprehensive means to implement current best practices for fuzzy-set qualitative comparative analysis robustness testing.
Keyphrases
  • social support
  • healthcare
  • mental health
  • depressive symptoms
  • systematic review
  • public health
  • primary care
  • health information
  • cross sectional
  • machine learning
  • deep learning
  • electronic health record
  • data analysis