Maternal identity measurement based on the experiences of mothers with infants: a methodological study

The developed tool demonstrated strong reliability and validity for assessing maternal identity in mothers of infants. It offers a structured approach for evaluating maternal identity in the context of infant care and has practical implications for maternal health research and interventions.

Authors

Institutions

Publication Details

Journal
Women s Health Nursing
Published
2025-03-28
DOI
https://doi.org/10.4069/whn.2025.03.08
Primary Topic
Family and Disability Support Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Maternal identity measurement based on the experiences of mothers with infants: a methodological study

Sun Jung Park, Eunyoung Choi
Women s Health Nursing
Family and Disability Support Research
article

Maternal identity measurement based on the experiences of mothers with infants: a methodological study

Sun Jung Park, Eunyoung Choi
article en

Abstract

The developed tool demonstrated strong reliability and validity for assessing maternal identity in mothers of infants. It offers a structured approach for evaluating maternal identity in the context of infant care and has practical implications for maternal health research and interventions.

Women s Health NursingVol. 31(1)
Sahmyook University (KR), Kyungdong University (KR)
National Research Foundation, National Research Foundation of Korea
Good health and well-being
Openalex Percentile: Top 92%
Family and Disability Support Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.