The types and effects of feedback received by emergency ambulance staff: Protocol for a systematic mixed studies review with narrative synthesis

Wilson, Caitlin, Janes, Gillian ORCID: https://orcid.org/0000-0002-1609-5898, Lawton, Rebecca and Benn, Jonathan (2021) The types and effects of feedback received by emergency ambulance staff: Protocol for a systematic mixed studies review with narrative synthesis. International Journal of Emergency Services, 10 (2). pp. 247-265. ISSN 2047-0894

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Abstract

Aim: The primary aim of this systematic review is to identify, describe and synthesize the published literature on the types and effects of feedback received by emergency ambulance staff. The secondary aim will be to describe the mechanisms and moderators of the effects of prehospital feedback in an organisational context. Introduction: The application and effects of feedback for healthcare professionals, to support improved practice, is well-researched within the wider healthcare domain. Within a prehospital context, research into feedback has been developing in specific areas such as automated feedback from defibrillators and debrief after simulation. However, to date there has been no systematic review published on the types and effects of feedback available to emergency ambulance staff. Methods: This study will be a systematic mixed studies review including empirical primary research of qualitative, quantitative and mixed-methods methodology published in peer-reviewed journals in English. Studies will be included if they explore the concept of feedback as defined in this review i.e. the systematised provision of information to emergency ambulance staff regarding their performance within prehospital practice and/or patient outcomes. The search strategy will consist of three facets: ambulance staff synonyms, feedback synonyms and feedback content. The databases to be searched from inception are MEDLINE, EMBASE, AMED, PsycInfo, HMIC, CINAHL and Web of Science. Study quality will be appraised using the Mixed Methods Appraisal Tool developed by Hong et al. (2018). Data analysis will consist of narrative synthesis guided by Popay et al. (2006) following a parallel-results convergent synthesis design. Registration: PROSPERO (CRD42020162600)