Background <p>The concept of fear of childbirth includes various types of anxiety and fears related to pregnancy and delivery. This fear can have a significant impact on women’s health. Therefore, a study was conducted to analyze the regression model of fear of childbirth among pregnant women.</p> Methods <p>This pilot cross-sectional study was conducted from August to November 2023 with 152 pregnant women in Bojnurd, Iran, using a multi-stage sampling method. Data were collected from tools including a personal and obstetric information form, a childbirth attitude questionnaire, social support assessments, and a childbirth self-efficacy scale. Data analysis was performed using SPSS version 24, utilizing descriptive statistics, t-tests, and multiple linear regression.</p> Results <p>The results of multiple linear regression indicate that the variables of age (B = 0.257), women’s education(B = 2.54), spouse’s education (B = 3.87), and preferred delivery(B = 7.097) can significantly predict the variance in fear of childbirth.</p> Conclusion <p>The results of this study can help healthcare providers identify and screen women at risk of experiencing fear of childbirth. By doing so, they can take proactive measures to reduce this fear and its negative effects, thereby easing the delivery process. This can be achieved through counseling sessions and by improving the quality of prenatal care for at-risk women.</p>

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Regression model of fear of childbirth in pregnant women

  • Farzaneh Rashidi,
  • Nazanin Hesari,
  • Sahar Shariatnia,
  • Abdollah Razi,
  • SeyyedMohammad MohammadiAubi,
  • Fatemeh Gorji,
  • Faezeh Ghanbari

摘要

Background

The concept of fear of childbirth includes various types of anxiety and fears related to pregnancy and delivery. This fear can have a significant impact on women’s health. Therefore, a study was conducted to analyze the regression model of fear of childbirth among pregnant women.

Methods

This pilot cross-sectional study was conducted from August to November 2023 with 152 pregnant women in Bojnurd, Iran, using a multi-stage sampling method. Data were collected from tools including a personal and obstetric information form, a childbirth attitude questionnaire, social support assessments, and a childbirth self-efficacy scale. Data analysis was performed using SPSS version 24, utilizing descriptive statistics, t-tests, and multiple linear regression.

Results

The results of multiple linear regression indicate that the variables of age (B = 0.257), women’s education(B = 2.54), spouse’s education (B = 3.87), and preferred delivery(B = 7.097) can significantly predict the variance in fear of childbirth.

Conclusion

The results of this study can help healthcare providers identify and screen women at risk of experiencing fear of childbirth. By doing so, they can take proactive measures to reduce this fear and its negative effects, thereby easing the delivery process. This can be achieved through counseling sessions and by improving the quality of prenatal care for at-risk women.