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Moderating Effect of General Self-Efficacy on the Relationship between Pregnancy Stress, Daily Hassles Stress, and Preterm Birth Risk in Women Experiencing Preterm Labor: A Cross-Sectional Study

  • Cho, Hyun-Jeong (Department of Nursing, Soonchunhyang University Cheonan Hospital) ;
  • Kim, Jeung-Im (School of Nursing, College of Medicine, Soonchunhyang University)
  • 투고 : 2024.01.16
  • 심사 : 2024.05.10
  • 발행 : 2024.08.31

초록

Purpose: This study investigated the moderating role of general self-efficacy (GSE) on how stress caused by pregnancy and daily hassle affect the risk of preterm birth (PTB) in women experiencing preterm labor. Methods: This cross-sectional study included 196 pregnant women experiencing preterm labor before 37 weeks of gestation. We used IBM SPSS Statistics 27 and employed Hayes process macro version 4 (model 1) and hierarchical regression to analyze the moderating effect of GSE on the relationship between pregnancy stress, daily hassle stress, and PTB risk. Results: Stress caused by pregnancy and daily hassle was positively correlated to PTB risk (r = .54, p < .001; r = .25, p < .001, respectively). While GSE did not significantly correlate with pregnancy stress, it negatively correlated with daily hassle stress (r = - .19, p = .009). GSE significantly moderated the relationship between combined stressors and PTB risk. As GSE levels increased, escalation in PTB risk in response to increasing stress levels was a more pronounced, highlighting a complex interaction between higher GSE levels and response to escalating stress levels. This model accounted for 39.5% of the variance in the PTB risk. Conclusion: Higher GSE may amplify the impact of stress on PTB risk, rather than mitigate it, which suggests a more nuanced role of GSE in the stress response of pregnant women at risk of preterm labor. GSE should be considered in care strategies, and managing its impact on stress perception and responses in pregnant women is crucial.

키워드

과제정보

We appreciate all women who participated in this study. For written English check, we used OpenAI. (2024). ChatGPT (4) [Large language model]. https://chat.openai.com.

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