Algorithmic Modeling of Caregiver Systemic Burden in Chronic Illness by Intersecting Partner Neuroticism and Dyadic Coping Mechanisms - Naseem, Aishath; Sargsyan, Arman; Hrivnáková, Zuzana (Applied Family Therapy Journal (AFTJ), 2026)
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.