Family caregiving and our aging population: caring for the family caregivers

Family caregivers play an indispensable role in healthcare systems, providing essential support to individuals with varying levels of dependency. This dissertation explores critical dimensions of caregiving through three interconnected studies, highlighting the challenges caregivers face, the factors influencing their utilization of support services, and the opportunities for intervention. The first study examines respite care utilization among 2,652 family caregivers using data from the National Study of Caregiving (NSOC). Findings reveal that financial, physical, and emotional difficulties significantly drive the use of respite care services. Participation in support groups is a predictor of respite care utilization for Non-White and Hispanic family caregivers, but not for their White, non-Hispanic counterparts. Emotional difficulties are a significant predictor of challenges for both males and females, with a stronger impact observed in females. Financial and physical difficulties are important predictors for female family caregivers but not for male family caregivers. Although respite care provides numerous benefits, it remains underutilized, indicating that substantial barriers prevent many individuals from accessing this essential service. The second study investigates the impact of formal caregiving training on 462 family caregivers of veterans. Using treatment effect estimation regression modeling, the study finds that training is significantly associated with reduced anxiety and loneliness and increased satisfaction and fulfillment in caregiving. The results underscore the need for culturally tailored and accessible training programs that emphasize caregiver autonomy and long-term well-being. The third study applies machine learning techniques to predict respite care utilization among 2,431 family caregivers. By addressing class imbalances in the dataset, models trained with the Synthetic Minority Oversampling Technique (SMOTE) demonstrate improved predictive accuracy, with the Boosted Tree-SMOTE model achieving the highest performance. This innovative application highlights the potential of predictive analytics to inform caregiver support strategies and optimize service delivery. Collectively, these studies emphasize the urgent need for policies and interventions that prioritize family caregiver well-being, expand access to support services, and leverage technological advancements in caregiving data. The thesis advocates for a holistic approach to caregiving, fostering a society that values and supports family caregivers as essential contributors to health and community resilience.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Community Health

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Ogunjesa, Babatope Ayokunle

Contributors dc:contributor

• Schwingel, Andiara
• Gobin, Robyn
• Raj, Minakshi
• Leao, Otavio

• Caregiving training
• caregiving
• family caregivers
• machine learning
• respite care
• informal caregiving

Statement dc:rights

• Copyright 2025 Babatope Ogunjesa

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129493

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129493