Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Introduction: Immigration and culture change have been thought to affect various aspects of psychological well-being, including eating behaviors. This study aimed to examine the association between immigration, acculturation strategies and eating patterns. Materials and Methods: Acculturation was conceptualized and measured by acculturation strategies of integration (maintaining original culture and adopting the new culture), assimilation (adopting the new culture and leaving behind the old), separation (sticking with the original culture only) and marginalization (maintaining/adopting neither culture). Eating patterns were conceptualized by dietary restriction, eating concern, shape concern, and weight concern. Links between demographic variables, acculturation strategies, and eating patterns were also examined. Five hundred and six Georgian women took part in the study: 253 living abroad (UK and USA) and 253 living in Georgia. Measures included East Asian Acculturation Measure (EAAM) for acculturation strategies (assimilation, integration, separation, and marginalization subscales) and Eating Disorder Examination Questionnaire (EDEQ) for eating patterns (dietary restriction, eating concern, weight concern, shape concern subscales, and global score). Relevant demographic variables and Body Mass Index (BMI) were recorded. Results: Comparisons of immigrant and nonimmigrant groups using Multivariate Analysis of Covariance (MANCOVA) with BMI as a covariate found a difference in dietary restriction only, with immigrants yielding higher mean score than non-immigrants. The global EDEQ scores of immigrant and nonimmigrant groups were almost identical though. Correlations between separation and marginalization and four EDEQ scores were statistically significant and positive, while correlations between integration and two EDEQ subscales were marginally significant and negative. Regression analysis showed that separation and marginalization strategies of acculturation were significantly linked with EDEQ eating concern, shape concern, weight concern, and global scores thereby representing predictors of elevated eating outcomes. Discussion: Findings suggested that moving to Western countries increased dietary restriction among Georgian women. Furthermore, while living abroad, the lack of integration in a host culture, as a common denominator of separation and marginalization strategies of acculturation, may predict elevated eating, shape, and weight concerns among women relocated over six years ago. Acculturation conditions may also be linked with integration or well-being outcomes.
Pubmed ID: 31551828
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Statistical modeling program that provides a wide choice of models, estimators, and algorithms in a program that has graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data, data that come from different populations with either observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program. The Mplus modeling framework draws on the unifying theme of latent variables. The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data.
View all literature mentions