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School of Nursing and Midwifery, East Africa


This study aims to determine key factors that predict resilience in older people. A cross-sectional design and quantitative methods were used for this study. Four districts were selected in Botswana using cluster random sampling. Data on resilience from 378 older adults aged 60 years+ [Mean Age (SD) = 71.1(9.0)] was collected using snowballing technique. Data on socio-demographics, protective and risk factors were also collected from urban and rural areas. CHAID (Chi-squared Automatic Interaction Detection) analysis was used to predict the strengths of the relationships among resilience and all predictor variables because the data were skewed. Five major predictor variables reached significance to be included in the model: depression, QOL, social impairment, education, and whether participants paid for services or accessed free services, along with high self-esteem (p < .001), security, and self-efficacy (p < .05). The presence of depression symptoms (χ2 = 23.7, p = .001, df = 1) and self-esteem (χ2 = 39.6, p < .001) had the greatest influence on resilience. Older people with no depression symptoms but had low QOL still had social impairment (χ2 = 3.9, p < .05). Older people with no depression symptoms had moderate to high QOL but had low resilience as a result of paying for services (χ2 = 7.4, p < .02). Both protective and risk factors had a significant influence on resilience. Knowledge about the predictors of resilience in older people may assist stakeholders devise effective intervention, especially now with COVID-19 ravaging the country. Additionally, policies and programs inclined to assist older people may be established and implemented.

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.