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    To explore moral distress empirically and conceptually, to understand the factors that mitigate and exacerbate moral distress and construct a model that represents how moral distress relates to its constituent parts and related concepts. There is ongoing debate about how to understand and respond to moral distress in nursing practice. The overarching design was feminist empirical bioethics in which feminist interpretive phenomenology provided the tools for data collection and analysis, reported following the COREQ guidelines. Using reflexive balancing, the empirical data were combined with feminist theory to produce normative recommendations about how to respond to moral distress. The Moral Distress Model presented in this paper is a culmination of the empirical data and theory. Using feminist interpretive phenomenology, critical care nurses in the United Kingdom (n = 21) were interviewed and data analysed. Reflexive Balancing was used to integrate the data with feminist theory to provide normative recommendations about how to understand moral distress. There are five compounding factors that exacerbate/ mitigate nurses' experiences of moral distress: epistemic injustice; the roster lottery; conflict between one's professional and personal responsibilities; ability to advocate and team dynamics. In addition to the causal connection and responses to moral distress, these factors make up the moral distress model which can guide approaches to mitigate moral distress. The Moral Distress Model is the culmination of these data and theorising formulated into a construct to explain how each element interacts. We propose that this model can be used to inform the design of interventions to address moral distress. © 2021 The Authors. Journal of Clinical Nursing published by John Wiley & Sons Ltd.

    Citation

    Georgina Morley, Caroline Bradbury-Jones, Jonathan Ives. The moral distress model: An empirically informed guide for moral distress interventions. Journal of clinical nursing. 2022 May;31(9-10):1309-1326

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    PMID: 34423483

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