Abstract
Background: Negative childbirth experiences impact the well-being of women and their families. With rising induction of labor (IOL) rates and the inconsistent evidence regarding its impact on childbirth experiences, we conducted a systematic review and meta-analysis to estimate the overall effect of IOL versus expectant management on women’s childbirth experiences, defined as a woman’s self-assessment of her lasting memories of the childbirth event.
Methods: We searched databases (PubMed, Embase, Cochrane Library, Science Direct, ProQuest and EBSCO) for RCTs and observational studies from 1970 to September 2023. Inclusion criteria covered women aged 19 and older with live, singleton, cephalic pregnancies at 37 0/7 until 41 6/7 weeks gestation. Quality was assessed using the Jadad and Newcastle-Ottawa Scales. RevMan 5.4 software and random-effects meta-analysis were used, adhering to PRISMA guidelines.
Results: Of the 1.467 screened articles, we included nine studies from five European countries. Six studies (2.376 women) used the Childbirth Experience Questionnaire (CEQ). Overall CEQ scores showed no significant IOL vs. expectant management differences (MD = 0.01 [95% CI -0.06, 0.08]; p=0.78; I2 = 65%). Subgroup analyses favored IOL in RCTs (MD = 0.07 [95% CI 0.02, 0.13]; p=0.006; I2 = 0%) and studies with ≥500 participants (MD = 0.09 [95% CI 0.02, 0.15]; p=0.006; I2 = 0%). Conversely, four studies (48.324 women) using the Childbirth Experience Visual Analogue Scale (VAS) favored expectant management (OR = 0.73 [95% CI 0.63, 0.85]; p<0.001; I2 = 86%).
Conclusion: VAS assessments suggest a more positive childbirth experience with the expectant management group. Improvements in the IOL process, transfer to delivery, and pain relief administration may benefit the IOL group. In contrast, the overall CEQ scores did not significantly differ between groups. Clinical practice should consider individualized approaches that align with patient needs and medical contexts. Limitations, such as variability in study quality and data heterogeneity, should be acknowledged.
