Date
12-4-2025
Department
School of Nursing
Degree
Doctor of Philosophy in Nursing (PhD)
Chair
Kara Schacke
Keywords
maternal-fetal training, maternal deserts, compassion fatigue, compassion satisfaction.
Disciplines
Nursing
Recommended Citation
Brown, Marissa Lemley, "Exploring the Relationship Between Maternal-Fetal Training and Maternal Desert Workers’ Compassion Fatigue: A Predictive Correlational Study" (2025). Doctoral Dissertations and Projects. 7731.
https://digitalcommons.liberty.edu/doctoral/7731
Abstract
The purpose of this quantitative, predictive correlational study was to explore the relationship between maternal-fetal training and maternal desert workers’ compassion satisfaction, burnout, and secondary traumatic stress in the southeastern region of the United States. Maternal deserts are defined as areas without dedicated obstetric services and impact more than 28 million childbearing-age women. These areas are becoming more prevalent, leaving healthcare workers to face caring for maternal-fetal health concerns in generalist areas. The study utilized a predictive correlational design to analyze relationships where multiple variables could influence the study. Stamm’s Professional Quality of Life V-5 (ProQOL) scale was used to gather information from 68 frontline workers who completed the survey through REDCap®. Participants were drawn from a convenience and purposeful sample of registered nurses (RN), paramedics, and emergency medical technicians (EMTs) located in 31 counties considered maternal deserts in Tennessee during 2025. SPSS was used to conduct descriptive statistical analysis. Pearson correlation and simple linear regression analyses revealed weak, non-statistically significant relationships between training hours and all three ProQOL domains. These findings suggest that maternal-fetal training hours alone do not significantly influence psychological outcomes in this population. Future research should be considered to expand the study findings, potentially replicating this study in a larger and more diverse sample across multiple states and healthcare settings. Engaging in a mixed-methods approach could enhance generalizability.
