Algorithmic Modeling of Caregiver Systemic Burden in Chronic Illness by Intersecting Partner Neuroticism and Dyadic Coping Mechanisms
Keywords:
Caregiver systemic burden, chronic illness, dyadic coping, neuroticismAbstract
Objective: To develop an algorithmic model predicting caregiver systemic burden by evaluating the complex, non-linear intersections between a care-recipient’s neuroticism and the couple’s dyadic coping mechanisms.
Methods and Materials: A cross-sectional, dyadic study design was utilized, comprising 214 caregiver/care-recipient dyads managing chronic illness in Yerevan, Armenia. Psychometric data were collected using the Zarit Burden Interview (ZBI) for caregiver burden, the Big Five Inventory-2 (BFI-2) for care-recipient neuroticism, and the Dyadic Coping Inventory (DCI) for dyadic coping. Predictive modeling was conducted using a Gradient Boosting Machine (GBM) algorithm with the dataset partitioned into training ( ) and testing ( ) subsets, while SHapley Additive exPlanations (SHAP) were employed to extract feature importance and interaction effects.
Findings: The GBM model exhibited strong predictive performance, accounting for of the variance in caregiver systemic burden on the test set ( ), with a Root Mean Square Error ( ) of . SHAP analyses identified care-recipient neuroticism as the most potent predictor of burden (Mean ), followed by caregiver engagement in negative dyadic coping (Mean ) and illness duration (Mean ). Interaction analyses revealed that the detrimental impact of partner neuroticism was exponentially amplified by negative dyadic coping and significantly buffered by active supportive dyadic coping.
Conclusion: Algorithmic modeling effectively demonstrates that caregiver systemic burden is a deeply interdependent phenomenon, highlighting that the psychological toll of a partner’s neuroticism is highly conditional upon the couple’s underlying dyadic coping architecture.
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References
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