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‘How to help your unwell child’: a sequential simulation project

‘How to help your unwell child’: a sequential simulation project

Imran, Ambreen, Holden, Ben, Weldon, Sharon Marie ORCID: 0000-0001-5487-5265, Boak, Duncan, Klaber, Bob, Kneebone, Roger, Watson, Mando and Bello, Fernando (2019) ‘How to help your unwell child’: a sequential simulation project. BMJ Simulation and Technology Enhanced Learning, 6 (2). pp. 127-128. ISSN 2056-6697 (doi:https://doi.org/10.1136/bmjstel-2018-000432)

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Abstract

During 2016–2017, National Health Service (NHS) England recorded 5.8 million attendances at accident and emergency departments (A&E) out of which 24% were deemed ‘avoidable’.1 Avoidable attendances are defined as an attendance at A&E resulting in care which could have been provided by a general practitioner (GP) or managed through self-care at home. ‘How to help your unwell child’ was a collaboration between Connecting Care for Children, and Imperial College Centre for Engagement and Simulation Science. The aim of the collaboration was to identify issues related to avoidable attendances at A&E in children under 5 years old in North West London. The overarching objective of the collaboration was to engage with local families to facilitate an understanding of their experiences of looking after unwell children suffering from minor illnesses.

A sequential simulation (SqS Simulation) event was organised at a community centre to engage with local families. It was used as a tool to engage healthcare professionals and families in acknowledging parental journeys and to recognise their concerns in seeking healthcare for minor childhood illnesses.

Item Type: Article
Uncontrolled Keywords: simulation, paediatrics, children, engagement
Subjects: L Education > L Education (General)
Faculty / Department / Research Group: Faculty of Education, Health & Human Sciences
Faculty of Education, Health & Human Sciences > Department of Adult Nursing & Paramedic Science
Last Modified: 02 Apr 2020 09:25
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/23690

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