Exploring the Relationship Between Compassion Satisfaction and Resilience Among Mental Health Nurses
Posted: Nov 05, 2022
Abstract
This study investigates the complex relationship between compassion satisfaction and resilience among mental health nurses, employing a novel computational framework that integrates psychometric analysis with machine learning approaches. While previous research has examined these constructs independently, our research introduces an innovative methodological approach that captures the dynamic, non-linear interactions between these psychological resources. We developed and validated the Compassion Satisfaction-Resilience Interaction Model (CS-RIM), which conceptualizes these constructs as mutually reinforcing systems rather than independent variables. Using a multi-site longitudinal design with 347 mental health nurses across diverse clinical settings, we collected comprehensive data through validated scales, ecological momentary assessments, and qualitative interviews over a 12-month period. Our computational analysis revealed three distinct resilience-satisfaction profiles: synergistic amplifiers, stable moderators, and compensatory buffers. The findings demonstrate that compassion satisfaction serves not merely as an outcome but as an active resilience-building mechanism, with bidirectional reinforcement patterns that challenge conventional linear models. The CS-RIM framework provides a sophisticated tool for healthcare organizations to develop targeted interventions that simultaneously enhance both compassion satisfaction and resilience, ultimately contributing to improved nurse retention and patient care quality in mental health settings. This research represents a significant methodological advancement in nursing psychology through its integration of computational modeling with traditional psychological assessment.
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