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Relationship between Employment Type and Self-Rated Health among Korean Immigrants in the US: Focusing on Gender and Number of Years in the US.

Sou Hyun Jang
Published in: International journal of environmental research and public health (2021)
Although Korean immigrants report worse self-rated health and a higher self-employment rate than other Asian immigrant groups, the relationship between their employment type and self-rated health is understudied. This study examines the relationship between employment type and self-rated health among Korean immigrants in the US. Survey data of 421 first-generation working-age (18-64 years old) Korean immigrants in the New York-New Jersey area were analyzed. The self-administrated survey questionnaire included 39 items (e.g., sociodemographic characteristics, self-rated health, and health insurance status). A logistic regression analysis was conducted to examine the relationship between the dependent variable-self-rated health (e.g., bad/not bad vs. good/very good)-and independent variable-employment type (e.g., work at non-ethnic firms, work at co-ethnic firms, self-employed, and unemployed)-by focusing on differences regarding gender and number of years living in the US. Self-employed and unemployed Korean immigrants were less likely to report good health compared to those working in non-ethnic firms. After controlling for sociodemographic characteristics (age, gender, marital status, education, health insurance status, membership in any Koran association, religion, and English proficiency), the relationship between employment type and self-rated health remained significant among female and recent Korean immigrants. More worksite interventions by occupational health nurses that target self-employed Korean immigrants, especially women and recent immigrants, are necessary.
Keyphrases
  • healthcare
  • mental health
  • public health
  • health insurance
  • health information
  • health promotion
  • human health
  • type diabetes
  • machine learning
  • pregnant women
  • adipose tissue
  • risk assessment
  • deep learning
  • climate change