Evidence map›Paper›PMID 38519910›Full record

ArticleBMC pregnancy and childbirth2024

Maternal performance after childbirth and its predictors: a cross sectional study.

Masoumeh Choobdarnezhad, Leila Amiri-Farahani, Sally Pezaro

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Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Masoumeh ChoobdarnezhadDepartment of Reproductive Health and Midwifery, School of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, Iran.
Leila Amiri-FarahaniDepartment of Reproductive Health and Midwifery, Nursing and Midwifery Care Research Center, School of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, 1996713883, Iran. l.amirifarahani@gmail.com.
Sally PezaroThe Research Centre for Healthcare and Communities, Coventry University, Coventry, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesBirthing parents need to use specialized skills as the first caregiver of the newborn. Several factors may affect performance. Yet there is a paucity of research in this area, and evidence remains inconsistent. Consequently, this study aimed to determine maternal performance after childbirth and its predictors.

methodsThis cross-sectional study was conducted with those (n = 450) who had given birth (< two months) and been referred for the vaccination of their newborn. The multi-stage sampling method was carried out from April 2022 to February 2023. Participants who met the inclusion criteria completed a demographic and obstetric information questionnaire, along with the childbirth experience 2 (CEQ2), Barkin maternal performance and maternal self-efficacy scales. Multiple linear regression was used to investigate the predictive effect of the independent variables of childbirth experience, maternal self-efficacy, demographic and obstetric variables on the dependent variable of maternal performance.

resultsThe mean age of the participants was 26.78 and the mean total score of maternal performance was 91.04 (0-120). The highest and lowest scores related to the 'maternal competence' and the 'maternal needs' domains, with mean score calculated at 77.51 and 72.81 respectively. 'Childbirth experience' and 'maternal self-efficacy' domains had a statistically significant relationship with maternal performance (P < 0.05). Among the predictive factors of maternal performance, the results of our linear regression demonstrated the variables of birth experience (B = 0.63), maternal self-efficacy (B = 1.53), spouse's employment status (B = 5.78 for worker level, B = 3.99 for employee level), the number of previous childbirth experiences (B = -8.46), frequency of receiving antenatal care (B = -6.68), length of stay in the birth suite (B = -2.22) and length of stay in the hospital (B = 2.84) remained in the model. 53.2% of changes in maternal performance can be explained by these independent variables.

conclusionThe promotion of evidence-based, person-centered, and respectful perinatal care during pregnancy and childbirth are of paramount importance. Strategies to improve the experience of childbirth and self-efficacy are especially required to improve maternal performance in the postpartum period. Prenatal care aimed at improving maternal function after childbirth will be important in achieving this overall.

Indexed as

Delivery, ObstetricPostpartum PeriodCross-Sectional StudiesFamilyFemaleHumansInfant, NewbornParturitionPregnancyPrenatal CareBirth experienceMaternal performanceMaternal self-efficacyPostpartumPredictors

Identifiers

PMID38519910
PMCPMC10960374

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.