Latent profile analysis of stress and resilience among rural women: A cross-sectional study
- PMID: 34750856
- DOI: 10.1111/phn.13005
Latent profile analysis of stress and resilience among rural women: A cross-sectional study
Abstract
Stress is a cardiovascular disease risk factor, and resilience may serve as a buffer for stress. Little is known about stress and resilience among rural women.
Objective: The purposes of this study were to identify profiles of rural women based upon indicators of psychosocial and environmental stress and to examine the relationships between the identified profiles and resilience.
Design and sample: A cross-sectional, descriptive design was used to explore stress, social support, and resilience among a representative sample of women (n = 354).
Measures: Data were collected to measure perceived stress, social support, chronic stress, and resilience.
Results: A latent profile analysis identified three profiles (59.9% Low Stress, 25.4% Moderate Stress, and 14.7% High Stress). Women in the High Stress profile were less likely to afford necessities and have attended college and more likely to be employed. Women in the Low Stress profile had the highest scores for all five resilience subscales.
Conclusion: The current study demonstrates the social and environmental impact of stress and how this stress can manifest differently for different women. Underserved women may benefit from strategies that reduce stress and improve social support and resilience. Future research is needed for advancing health equity in rural populations.
Keywords: resilience; rural health; stress.
© 2021 Wiley Periodicals LLC.
References
REFERENCES
-
- Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332. https://doi.org/10.1007/BF02294359
-
- Albert, M. A., Durazo, E. M., Slopen, N., Zaslavsky, A. M., Buring, J. E., Silva, T., Chasman, D., & Williams, D. R. (2017). Cumulative psychological stress and cardiovascular disease risk in middle aged and older women: Rationale, design, and baseline characteristics. American Heart Journal, 192, 1-12. https://doi.org/10.1016/j.ahj.2017.06.012
-
- Asparouhov, T., & Muthen, B. (2013). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling, 21, 329-341. https://doi.org/10.1080/10705511.2014.915181
-
- Bardach, S. H., Tarasenko, Y. N., & Schoenberg, N. E. (2011). The role of social support in multiple morbidity: Self-management among rural residents. Journal of Health Care for the Poor and Underserved, 22(3), 756-771. https://doi.org/10.1353/hpu.2011.0083
-
- Bollen, K. (1989). Structural equations with latent variables. New York, NY: Wiley.
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
