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Sequential simulation: a new approach to sharing challenges, opportunities and learning in maternity service delivery

Sequential simulation: a new approach to sharing challenges, opportunities and learning in maternity service delivery

Kronfli, Miranda, Weldon, S. M. ORCID: 0000-0001-5487-5265, Kneebone, R. and Bello, F. (2017) Sequential simulation: a new approach to sharing challenges, opportunities and learning in maternity service delivery. In: BMJ STEL. BMJ Publishing Group Limited, A13-A14. ISSN 2056-6697 (Online) (doi:https://doi.org/10.1136/bmjstel-2017-aspihconf.27)

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Abstract

A recent CQC inspection identified disparities in care quality across maternity services in several London1 districts, revealing a need to identify challenges and solutions and to share learning across sites. Sequential Simulation2 (SqS) uses physical simulation to portray selected snapshots from a pathway of care, for pedagogical and engagement activities. In representing the lived experience of service users, SqS provides a platform for facilitated discussion among a diverse range of multidisciplinary participants. SqS workshops at six maternity sites in London aimed to explore service delivery challenges with front-line staff, in order to identify possible solutions, establish whether findings could inform learning outcomes for an educational intervention, and identify unanticipated outcomes of facilitated discussions.

Item Type: Conference Proceedings
Title of Proceedings: BMJ STEL
Additional Information: Abstracts of the Association for Simulation Practice in Healthcare Annual Conference, 6th to 7th November 2017, Telford, UK.
Uncontrolled Keywords: Sequential Simulation; Simulation
Faculty / Department / Research Group: Faculty of Education & Health
Faculty of Education & Health > Department of Adult Nursing & Paramedic Science
Faculty of Education & Health > Health & Society Research Group
Last Modified: 28 May 2018 02:23
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/19285

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