Women's views and experiences of augmentation of labour with synthetic oxytocin infusion. A protocol for a qualitative evidence synthesis.
Silvia Alòs-PereñíguezDeirdre O'MalleyDeirdre DalyPublished in: HRB open research (2022)
Background: Augmentation of labour (AOL) is the most common intervention to treat labour dystocia. Previous research reported extensive disparities in AOL rates across countries and institutions. Despite its widespread use, women's views on and experiences of intrapartum augmentation with infused synthetic oxytocin are limited. Methods: A qualitative evidence synthesis on women's views and experiences of AOL with synthetic oxytocin after spontaneous onset of labour will be conducted. Qualitative studies and studies employing a mixed methods design, where qualitative data can be extracted separately, will be included, as will surveys with open-ended questions that provide qualitative data. A systematic search will be performed of the databases: MEDLINE, CINAHL, EMBASE, PsycINFO, Maternity and Infant Care and Web of Science Core Collection from the date of inception. The methodological quality of included studies will be assessed using the Evidence for Policy and Practice Information and Co-ordinating Centre's appraisal tool. A three-stage approach, coding of data from primary studies, development of descriptive themes and generation of analytical themes, will be used to synthesise findings. Confidence in findings will be established by the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research. Discussion: This qualitative evidence synthesis may provide valuable information on women's experiences of AOL and contribute to a review of clinical practice guidelines for maternity care providers. PROSPERO registration: CRD42021285252 (14/11/2021).
Keyphrases
- polycystic ovary syndrome
- healthcare
- mental health
- pregnancy outcomes
- case control
- big data
- systematic review
- electronic health record
- randomized controlled trial
- quality improvement
- public health
- palliative care
- cervical cancer screening
- cross sectional
- low dose
- insulin resistance
- breast cancer risk
- type diabetes
- soft tissue
- machine learning
- metabolic syndrome
- pregnant women
- minimally invasive
- skeletal muscle
- deep learning
- social media
- health insurance
- data analysis