Selected article for: "logistic regression and london university"

Author: Mak, H W; Fancourt, D
Title: Predictors of engaging in voluntary work during the COVID-19 pandemic: analyses of data from 31,890 adults in the UK.
  • Cord-id: dov6n4sy
  • Document date: 2021_4_15
  • ID: dov6n4sy
    Snippet: AIMS As the COVID-19 pandemic has grown internationally, there has been an increased need for volunteers. This study aimed to identify the predictors of volunteering including demographic backgrounds, socio-economic characteristics, personality, and psychosocial factors. METHODS Data were analysed from 31,890 adults in the UK COVID-19 Social Study run by the University College London - a longitudinal study focusing on the psychological and social experiences of adults living in the UK during the
    Document: AIMS As the COVID-19 pandemic has grown internationally, there has been an increased need for volunteers. This study aimed to identify the predictors of volunteering including demographic backgrounds, socio-economic characteristics, personality, and psychosocial factors. METHODS Data were analysed from 31,890 adults in the UK COVID-19 Social Study run by the University College London - a longitudinal study focusing on the psychological and social experiences of adults living in the UK during the COVID-19 pandemic. Tetrachoric factor analysis was applied to identify latent categories of voluntary work. Multivariate logistic regression was used to identity predictors for volunteering and change in volunteering behaviours since before the COVID-19 pandemic. RESULTS Three types of volunteering during the pandemic were identified as follows: formal volunteering, social action volunteering, and neighbourhood volunteering. Regression analysis showed that the pattern of voluntary work was structured by demographic backgrounds, socio-economic factors, personality, and psychosocial factors. CONCLUSION The predictors of volunteering during the pandemic may be slightly different from other non-emergency period.

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