National Centre of Competence in Life Course Research LIVES, Institute of Social Sciences, University of Lausanne, Lausanne, Switzerland.
J Community Psychol. 2022 Sep;50(8):3560-3573. doi: 10.1002/jcop.22855. Epub 2022 Mar 31.
We examined the extent to which intersectional social identities combine to shape risks of loneliness and identified the specific social clusters that are most at risk of loneliness for more precise and targeted interventions to reduce loneliness in a Swiss municipality. Based on data collected using participatory action research, we used the novel multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to estimate the predictive power of intersectional social attributes on risk of loneliness. We found that 56% of the between-strata variance was captured by intersectional interaction but was not explained by the additive effect of social identities. We also found that nationality and education had the strongest predictive power for loneliness. Interventions to reduce loneliness may benefit from understanding the resident population's intersectional identities given that individuals with the same combinations of social identities face a common set of social exposures relating to loneliness.
我们考察了交叉社会身份在多大程度上共同塑造孤独风险，并确定了特定的社会群体，这些群体最容易感到孤独，以便更精确和有针对性地进行干预，以减少瑞士一个市镇的孤独感。基于使用参与式行动研究收集的数据，我们使用新颖的个体异质性和歧视准确性的多层次分析（MAIHDA）来估计交叉社会属性对孤独风险的预测能力。我们发现，56%的层间方差是由交叉交互捕获的，但不能用社会身份的加性效应来解释。我们还发现，国籍和教育对孤独感具有最强的预测能力。减少孤独感的干预措施可能受益于了解居民的交叉身份，因为具有相同社会身份组合的个体面临着与孤独感相关的一组共同的社会暴露。
