IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v12y2022i3p21582440221127145.html
   My bibliography  Save this article

Resilience: Key Factors Associated With Resilience of Older People in Botswana

Author

Listed:
  • Magen Mhaka-Mutepfa
  • Sheila Shaibu

Abstract

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  

Suggested Citation

  • Magen Mhaka-Mutepfa & Sheila Shaibu, 2022. "Resilience: Key Factors Associated With Resilience of Older People in Botswana," SAGE Open, , vol. 12(3), pages 21582440221, September.
    • Handle: RePEc:sae:sagope:v:12:y:2022:i:3:p:21582440221127145
    DOI: 10.1177/21582440221127145
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440221127145
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440221127145?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Harms, P. D. & Wood, Dustin, 2016. "Bouncing Back to the Future: A Look at the Road Ahead for the Assessment of Resilience," Industrial and Organizational Psychology, Cambridge University Press, vol. 9(2), pages 436-442, June.
    2. Rachel Pruchno & Deborah Carr, 2017. "Successful Aging 2.0: Resilience and Beyond," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 72(2), pages 201-203.
    3. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    4. Elena Caroline Weitzel & Margrit Löbner & Susanne Röhr & Alexander Pabst & Ulrich Reininghaus & Steffi G. Riedel-Heller, 2021. "Prevalence of High Resilience in Old Age and Association with Perceived Threat of COVID-19—Results from a Representative Survey," IJERPH, MDPI, vol. 18(13), pages 1-9, July.
    5. Heidi Igarashi & Maria L Kurth & Hye Soo Lee & Soyoung Choun & Dylan Lee & Carolyn M Aldwin, 2022. "Resilience in Older Adults During the COVID-19 Pandemic: A Socioecological Approach," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 77(4), pages 64-69.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    2. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    3. Yousaf Muhammad & Dey Sandeep Kumar, 2022. "Best proxy to determine firm performance using financial ratios: A CHAID approach," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 219-239, September.
    4. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    5. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    6. Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, December.
    7. H Seol & H Lee & S Kim & Y Park, 2008. "The impact of information technology on organizational efficiency in public services: a DEA-based DT approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 231-238, February.
    8. Vanhoucke, Mario & Maenhout, Broos, 2009. "On the characterization and generation of nurse scheduling problem instances," European Journal of Operational Research, Elsevier, vol. 196(2), pages 457-467, July.
    9. Todor Krastevich, 2013. "Using Predictive Modeling to Improve Direct Marketing Performance," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 25-55.
    10. Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    11. Adrien Ehrhardt & Christophe Biernacki & Vincent Vandewalle & Philippe Heinrich, 2019. "Feature quantization for parsimonious and interpretable predictive models," Papers 1903.08920, arXiv.org.
    12. Onur Doğan & Hakan Aşan & Ejder Ayç, 2015. "Use Of Data Mining Techniques In Advance Decision Making Processes In A Local Firm," European Journal of Business and Economics, Central Bohemia University, vol. 10(2), pages 6821:10-682, January.
    13. Jae-Dong Kim & Tae-Hyeong Kim & Sung Won Han, 2023. "Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks," Mathematics, MDPI, vol. 11(3), pages 1-10, January.
    14. Agapito, Dora & Mendes, Julio & Valle, Patricia, 2011. "The Sea as a Connection between Residents and Tourists in Coastal Destinations: A Case in Algarve," Spatial and Organizational Dynamics Discussion Papers 2011-13, CIEO-Research Centre for Spatial and Organizational Dynamics, University of Algarve.
    15. Francisco Javier Rondán-Cataluña & Patricio E. Ramírez-Correa & Jorge Arenas-Gaitán & Muriel Ramírez-Santana & Elizabeth E. Grandón & Jorge Alfaro-Pérez, 2020. "Social Network Communications in Chilean Older Adults," IJERPH, MDPI, vol. 17(17), pages 1-17, August.
    16. Timmins, Christopher & Vissing, Ashley, 2022. "Environmental justice and Coasian bargaining: The role of race, ethnicity, and income in lease negotiations for shale gas," Journal of Environmental Economics and Management, Elsevier, vol. 114(C).
    17. A. Israëls & J. Driel, 1983. "Use of the chi-square statistic for selecting explanatory variables in multiway tables," Quality & Quantity: International Journal of Methodology, Springer, vol. 17(2), pages 103-116, April.
    18. Nocella, Giuseppe & Stefani, Gianluca & Romano, Donato, 2011. "Preferences, trust and willingness to pay for food information: An analysis of the Italian Market," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114606, European Association of Agricultural Economists.
    19. Auld, Joshua & Mohammadian, Abolfazl (Kouros) & Doherty, Sean T., 2009. "Modeling activity conflict resolution strategies using scheduling process data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 386-400, May.
    20. Marina Segura & Jorge Mello & Adolfo Hernández, 2022. "Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?," Mathematics, MDPI, vol. 10(18), pages 1-20, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:12:y:2022:i:3:p:21582440221127145. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.