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The purpose of this study is to employ a mixed methods research approach combining quantitative and qualitative methods to thoroughly understand the relationships among caregiver burden, quality of life, family functioning, and family resilience of children with medical complexity (CMC). The study aims to explore the individual and social factors influencing these variables and investigate the factors impacting caregiver burden, quality of life, family functioning, and family resilience of CMC caregivers. Finally, an integrative analytical framework will be proposed. The study seeks to provide guidance and recommendations for social support for caregivers of children with medical complexity in caregiving practices, aiming to promote the development of relevant policies and services.
Full description
This study employs a convergent parallel mixed methods design. The quantitative part involves a questionnaire survey using purposive and snowball sampling methods, inviting 400 caregivers of children with medical complexity (CMC) to participate. Stratified sampling is then applied, with 62 samples selected per age group (1-5 years, 6-10 years, 11-15 years, 16-20 years), resulting in a total of 248 valid samples. The aim is to measure the correlations among caregiver burden, quality of life, family functioning, and family resilience. For the qualitative research, purposive and snowball sampling methods are also used to recruit 10 caregivers of CMC from different age groups who meet the inclusion criteria and agree to participate. These caregivers undergo semi-structured in-depth interviews, with interview content recorded to gain deeper insights into their experiences and feelings regarding caregiver burden, quality of life, family functioning, and family resilience. The study instruments include validated tools such as the Caregiver Burden Inventory (CBI), SF-12 Health Survey for quality of life, the APGAR Family Functioning Scale, and a Chinese version of the Family Resilience Scale. Qualitative data are collected through semi-structured interviews. Data analysis involves descriptive statistics, analysis of variance (ANOVA), and multiple linear regression models using IBM SPSS Statistics 27 for quantitative data. Qualitative data are analyzed using textual and thematic analysis methods, supported by NVivo qualitative analysis software. Finally, data triangulation is employed to integrate and compare quantitative and qualitative findings, leading to conclusions and recommendations.
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Inclusion criteria
Note: Inclusion criteria are the same for both quantitative and qualitative studies.
Exclusion criteria
Note: Exclusion criteria are the same for both quantitative and qualitative studies.
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Central trial contact
ChingWen Cheng, MD
Data sourced from clinicaltrials.gov
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