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A Meta-Analysis of Interventions to Reduce Loneliness:

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TLDR
An integrative meta-analysis of loneliness reduction interventions was conducted to quantify the effects of each strategy and to examine the potential role of moderator variables, and revealed that single-group pre-post and nonrandomized comparison studies yielded larger mean effect sizes relative to randomized comparison studies.
Abstract
Social and demographic trends are placing an increasing number of adults at risk for loneliness, an established risk factor for physical and mental illness. The growing costs of loneliness have led to a number of loneliness reduction interventions. Qualitative reviews have identified four primary intervention strategies: (a) improving social skills, (b) enhancing social support, (c) increasing opportunities for social contact, and (d) addressing maladaptive social cognition. An integrative meta-analysis of loneliness reduction interventions was conducted to quantify the effects of each strategy and to examine the potential role of moderator variables. Results revealed that single-group pre-post and nonrandomized comparison studies yielded larger mean effect sizes relative to randomized comparison studies. Among studies that used the latter design, the most successful interventions addressed maladaptive social cognition. This is consistent with current theories regarding loneliness and its etiology. Theoretical and methodological issues associated with designing new loneliness reduction interventions are discussed.

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The Roles of Marital Dissolution and Subsequent Repartnering on Loneliness in Later Life.

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Loneliness among adolescents and young adults with cancer during the COVID-19 pandemic: a cross-sectional survey.

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Minority Stress and Loneliness in a Global Sample of Sexual Minority Adults: The Roles of Social Anxiety, Social Inhibition, and Community Involvement

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Book

Practical Meta-Analysis

TL;DR: This paper presents a meta-analysis procedure called “Meta-Analysis Interpretation for Meta-Analysis Selecting, Computing and Coding the Effect Size Statistic and its applications to Data Management Analysis Issues and Strategies.
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