Decoding Caregiver Burden in Cancer: Role of Emotional Health, Rumination, and Coping Mechanisms.
Ipek Özönder ÜnalÇetin OrduPublished in: Healthcare (Basel, Switzerland) (2023)
This study aimed to elucidate the role of psychological factors in caregiver burden among caregivers of stage 4 cancer patients. Data were collected from 328 caregivers of cancer patients, employing the Zarit Care Burden Scale, Depression-Anxiety-Stress Scale (DASS-42), Dysfunctional Attitudes Scale (DAS-A), Ruminative Thought Style Questionnaire (RTSQ), and Coping Orientation to Problems Experienced Inventory (Brief COPE). Males, spouses, and caregivers of patients with a PEG or tracheostomy, or those diagnosed with pancreatic biliary cancer were found to have a significantly higher risk of caregiver burden. Age, sex, caregiver-patient relationship, caregiving duration, patient's catheter status, cancer types, depression and stress severity, rumination, dysfunctional attitudes, and dysfunctional coping strategies explained 69.7% of the variance in Zarit Care Burden Scale scores (F(14,313) = 51.457, p < 0.001), illustrating their significant predictive relationship with caregiver burden. Moderation analysis revealed significant interactions of emotional coping with depression (b = -0.0524, p = 0.0076) and dysfunctional coping with stress on caregiver burden (b = 0.014, p = 0.006). Furthermore, rumination mediated the relationships between caregiver burden, stress, and depression ( p < 0.01). Overall, the results highlight the intricate relationships among caregiver burden, mental health, and coping strategies, suggesting tailored interventions to support caregiver health and quality of care.
Keyphrases
- depressive symptoms
- mental health
- palliative care
- healthcare
- social support
- sleep quality
- papillary thyroid
- risk factors
- quality improvement
- squamous cell carcinoma
- intensive care unit
- machine learning
- rheumatoid arthritis
- mental illness
- drug delivery
- climate change
- artificial intelligence
- young adults
- big data
- smoking cessation
- deep learning