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Typology and correlates of parental stress among caregivers of children with DBDs in low-resourced communities in Uganda

Author

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  • Rachel Brathwaite
  • Natasja Magorokosho
  • Flavia Namuwonge
  • Nhial Tutlam
  • Torsten B Neilands
  • Mary M McKay
  • Fred M Ssewamala

Abstract

Disruptive Behavior Disorders (DBDs) is one of the most common mental health problems among children in Uganda and SSA. Yet, to our knowledge no research has studied parenting stress (PS) among caregivers of children with DBDs, or investigated which risk factors originate from the child, parent, and contextual environment. Using a rigorous analytical approach, we aimed to: 1) identify different types and; 2) examine factors associated with PS and how correlates differ according to the type of stress experienced among caregivers of children with DBDs in low-resourced Ugandan communities. We used data from 633 caregivers of children with DBDs from SMART-Africa Uganda study. PS, was measured using the 36-item Parenting Stress Index-Short Form (PSI-SF). To identify focal correlates related to child/parent/contextual environment, we performed variable importance screening using the Stata command -gvselect- and specified mixed/melogit multilevel modeling with random effects. Secondly, focal correlates were included in the cross-fit partialing out lasso linear/logistic regression (double machine-learning) model. Caregivers mostly experienced stress from parental distress and caring for a child with difficult behavior. As scores increased by one unit on: caregiver mental health distress, PSI-SF increased by 0.23 (95% CI = 0.15, 0.32) (reflecting higher stress levels); Child difficulties, PSI-SF increased by 0.77 (95% CI = 0.52, 1.02). Contrastingly, for every one unit increase in family cohesion scores, PSI-SF decreased by 0.54 (95% CI = -0.84, -0.23). Caregivers with college/diploma/undergraduate/graduate education had less stress than those completing primary only or never attended school [Coefficient = -8.06 (95% CI = -12.56, -3.56)]. Family financial supporters had significantly higher Parental distress than caregivers who were not [Coefficient = 2.68 (95% CI = 1.20, 4.16)]. In low-resource settings like Uganda where mental health support is limited, community-based family-focused and economic empowerment interventions that improve community support systems and address financial barriers can reduce stress levels of caregivers of children with DBDs.

Suggested Citation

  • Rachel Brathwaite & Natasja Magorokosho & Flavia Namuwonge & Nhial Tutlam & Torsten B Neilands & Mary M McKay & Fred M Ssewamala, 2023. "Typology and correlates of parental stress among caregivers of children with DBDs in low-resourced communities in Uganda," PLOS Global Public Health, Public Library of Science, vol. 3(8), pages 1-17, August.
    • Handle: RePEc:plo:pgph00:0002306
    DOI: 10.1371/journal.pgph.0002306
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Nabunya, Proscovia & Ssewamala, Fred M. & Ilic, Vilma, 2014. "Family economic strengthening and parenting stress among caregivers of AIDS-orphaned children: Results from a cluster randomized clinical trial in Uganda," Children and Youth Services Review, Elsevier, vol. 44(C), pages 417-421.
    3. Ice, Gillian H. & Sadruddin, Aalyia F.A. & Vagedes, Amy & Yogo, Jaja & Juma, Elizabeth, 2012. "Stress associated with caregiving: An examination of the stress process model among Kenyan Luo elders," Social Science & Medicine, Elsevier, vol. 74(12), pages 2020-2027.
    4. Charles Lindsey & Simon Sheather, 2015. "Best subsets variable selection in nonnormal regression models," Stata Journal, StataCorp LLC, vol. 15(4), pages 1046-1059, December.
    5. Nabunya, Proscovia & Ssewamala, Fred M. & Ilic, Vilma, 2014. "Reprint of “Family economic strengthening and parenting stress among caregivers of AIDS-orphaned children: Results from a cluster randomized clinical trial in Uganda”," Children and Youth Services Review, Elsevier, vol. 47(P2), pages 182-186.
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