Implementing the Maternal Postnatal Attachment Scale (MPAS) in universal services: Qualitative interviews with health visitors.
Philippa K BirdZoe HindsonAbigail DunnAnna Cronin de ChavezJosie DickersonJoanna HowesTracey BywaterPublished in: Wellcome open research (2022)
Background : A secure parent-infant relationship lays the foundations for children's development, however there are currently no measurement tools recommended for clinical practice. We evaluate the clinical utility of a structured assessment of the parent-infant relationship (the Maternal Postnatal Attachment Scale, MPAS) in a deprived, multi-ethnic urban community in England. This paper answers the question: what are health visitors' views on the parent-infant relationship, and experiences of piloting the MPAS? It explores the barriers and facilitators to implementation, and complements the paper on psychometric properties and representativeness reported in Dunn et al (submitted). Methods : Semi-structured interviews were conducted with 11 health visitors and data were analysed using thematic analysis. Results: Health visitors saw identification and support of the parent-infant relationship as an important part of their role, and reported benefits of the MPAS, including opening conversation, and identifying and reporting concerns. Challenges included timing and workload, the appropriateness of language, perceived intrusiveness and understanding of the questions, and the length of the tool. Suggestions for improvements to the tool were put forwards. Conclusions: The experiences, benefits and challenges identified help to explain results in Dunn et al , and the wide-ranging challenges identified would hinder assessment of the parent-infant relationship in routine practice. Further work with health professionals and parents has been undertaken to co-produce an acceptable, feasible and reliable tool for clinical practice.
Keyphrases
- healthcare
- mental health
- clinical practice
- public health
- primary care
- health information
- psychometric properties
- preterm infants
- quality improvement
- depressive symptoms
- birth weight
- machine learning
- pregnancy outcomes
- young adults
- pregnant women
- risk assessment
- electronic health record
- weight loss
- gestational age
- weight gain