Moral distress in midwifery practice: A concept analysis.
Wendy FosterLois McKellarJulie-Anne FleetLinda SweetPublished in: Nursing ethics (2021)
Research suggests that the incidence of moral distress experienced by health professionals is significant and increasing, yet the concept lacks clarity and remains largely misunderstood. Currently, there is limited understanding of moral distress in the context of midwifery practice. The term moral distress was first used to label the psychological distress experienced following complex ethical decision-making and moral constraint in nursing. The term is now used across multiple health professions including midwifery, nursing, pharmacy and medicine, yet is used cautiously due to confusion regarding its theoretical and contextual basis. The aim of this study is to understand the concept of moral distress in the context of midwifery practice, describing the attributes, antecedents and consequences. This concept analysis uses Rodgers' evolutionary framework and is the first stage of a sequential mixed-methods study. A literature search was conducted using multiple databases resulting in eight articles for review. Data were analysed using NVivo12©. Three core attributes were identified: moral actions and inactions, conflicting needs and negative feelings/emotions. The antecedents of clinical situations, moral awareness, uncertainty and constraint were identified. Consequences of moral distress include adverse personal professional and organisational outcomes. A model case depicting these aspects is presented. A midwifery focused definition of moral distress is offered as 'a psychological suffering following clinical situations of moral uncertainty and/or constraint, which result in an experience of personal powerlessness where the midwife perceives an inability to preserve all competing moral commitments'. This concept analysis affirms the presence of moral distress in midwifery practice and provides evidence to move towards a consistent definition of moral distress.
Keyphrases
- decision making
- healthcare
- primary care
- public health
- systematic review
- mental health
- clinical trial
- quality improvement
- gene expression
- emergency department
- type diabetes
- risk factors
- risk assessment
- skeletal muscle
- machine learning
- adipose tissue
- insulin resistance
- physical activity
- gestational age
- depressive symptoms
- climate change
- deep learning
- data analysis
- glycemic control