Translation, validation, and factor structure of the Nepali version of postpartum bonding questionnaires (PBQ-N) among postpartum women in Nepal.
Sangita Pudasainee-KapriTumla ShresthaThomas DahanMary WunnenbergPublished in: PLOS global public health (2024)
This study aimed to translate and test the psychometric properties of the Nepali version of the PBQ (PBQ-N) among postpartum mothers in Kathmandu, Nepal. Data was collected through semi-structured interviews with postpartum mothers (n = 128) of an infant aged one to six months visiting immunization clinics at two selected hospitals in Kathmandu, Nepal. The PBQ scale was translated into Nepali language and backtranslated to English with the help of language and content experts. The PBQ-N was then assessed for factor structure, validity, and reliability. The exploratory factor analysis (EFA) was conducted to examine construct validity of the PBQ-N in which 16 items (α = .75) of the original 25 items grouped into three subscales and were found suitable to measure mother-infant bonding among Nepalese women. Regarding convergent validity, a statistically significant, positive correlation was found between the PBQ-N and postpartum depression (r = .627, p < .001). In addition, a statistically significant, negative association was found between parenting self-efficacy and the PBQ-N (r = -.496, p < .001). The three subscales demonstrated good internal consistency. Findings indicate adequate estimates of validity and reliability for the PBQ-N in which 16-item measures were considered adequate for screening mother-infant bonding among Nepalese women and are useful for clinical and research purposes. Considering the crucial role of maternal-infant bonding relationships, the use of validated measures is recommended to screen high-risk infants in clinical settings.
Keyphrases
- psychometric properties
- polycystic ovary syndrome
- pregnancy outcomes
- healthcare
- tertiary care
- primary care
- depressive symptoms
- high throughput
- physical activity
- pregnant women
- insulin resistance
- machine learning
- electronic health record
- metabolic syndrome
- cervical cancer screening
- body mass index
- breast cancer risk
- skeletal muscle
- sleep quality
- single cell