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From Willingness to Readiness: Caregiver Activation for Cancer Care in Singapore
Safora Johansen1,2,3, Melissa Gaik Ming Ooi4,5, Alice F S Chua6
1Faculty of Health Sciences, Oslo Metropolitan University, 0167 Oslo, Norway.
Background:
Family caregivers are essential partners in the outpatient care of older adults with cancer, yet the knowledge, skills, and confidence, collectively, of caregiver activation are not well characterized in Asian settings. Understanding activation can inform tailored support to sustain effective caregiving. Accordingly, this study assessed the overall activation levels among cancer caregivers in the three most common cancer diagnoses in Singapore by using the Caregiver-Patient Activation Measure (CG-PAM).
Methods:
A total of 103 informal caregivers of patients ≥60 years (lung, GI, or myeloma) at Singapore's largest public cancer hospitals completed the 13-item CG-PAM, scored 0-100 and classified into four activation levels. Descriptive statistics summarized characteristics and CG-PAM responses, and logistic regression analyses assessed the degree of activation for individual demographic and other characteristics (p < 0.05).
Results:
Caregivers showed moderate-high activation (mean 65.9 ± 16.1) and the following levels: L1, 4.9%; L2, 23.3%; L3, 38.8%; L4, 31.1%. They strongly endorsed personal responsibility and active engagement, reporting confidence in key self-management tasks, but struggled to sustain behaviors under stress. Activation was not significantly associated with demographic factors or any other measured characteristics.
Conclusions:
Caregivers of older adults with cancer in Singapore included in this study showed moderate-high activation and strong self-management confidence but struggled to sustain behaviors under stress. Routine activation assessment and tailored support (education, coaching) could strengthen outpatient care. Longitudinal and interventional research, alongside follow-up interviews, is needed to identify unmet needs, existing support systems, and inform scalable, sustainable models.
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