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A prospective, mixed-methods, before and after study to identify the evidence base for the core components of an effective Paediatric Early Warning System and the development of an implementation…

Overview of attention for article published in BMC Pediatrics, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Title
A prospective, mixed-methods, before and after study to identify the evidence base for the core components of an effective Paediatric Early Warning System and the development of an implementation package containing those core recommendations for use in the UK: Paediatric early warning system – utilisation and mortality avoidance– the PUMA study protocol
Published in
BMC Pediatrics, July 2018
DOI 10.1186/s12887-018-1210-z
Pubmed ID
Authors

Emma Thomas-Jones, Amy Lloyd, Damian Roland, Gerri Sefton, Lyvonne Tume, Kerry Hood, Chao Huang, Dawn Edwards, Alison Oliver, Richard Skone, David Lacy, Ian Sinha, Jenny Preston, Brendan Mason, Nina Jacob, Robert Trubey, Heather Strange, Yvonne Moriarty, Aimee Grant, Davina Allen, Colin Powell

Abstract

In hospital, staff need to routinely monitor patients to identify those who are seriously ill, so that they receive timely treatment to improve their condition. A Paediatric Early Warning System is a multi-faceted socio-technical system to detect deterioration in children, which may or may not include a track and trigger tool. It functions to monitor, detect and prompt an urgent response to signs of deterioration, with the aim of preventing morbidity and mortality. The purpose of this study is to develop an evidence-based improvement programme to optimise the effectiveness of Paediatric Early Warning Systems in different inpatient contexts, and to evaluate the feasibility and potential effectiveness of the programme in predicting deterioration and triggering timely interventions. This study will be conducted in two district and two specialist children's hospitals. It deploys an Interrupted Time Series (ITS) design in conjunction with ethnographic cases studies with embedded process evaluation. Informed by Translational Mobilisation Theory and Normalisation Process Theory, the study is underpinned by a functions based approach to improvement. Workstream (1) will develop an evidence-based improvement programme to optimise Paediatric Early Warning System based on systematic reviews. Workstream (2) consists of observation and recording outcomes in current practice in the four sites, implementation of the improvement programme and concurrent process evaluation, and evaluation of the impact of the programme. Outcomes will be mortality and critical events, unplanned admission to Paediatric Intensive Care (PICU) or Paediatric High Dependency Unit (PHDU), cardiac arrest, respiratory arrest, medical emergencies requiring immediate assistance, reviews by PICU staff, and critical deterioration, with qualitative evidence of the impact of the intervention on Paediatric Early Warning System and learning from the implementation process. This paper presents the background, rationale and design for this mixed methods study. This will be the most comprehensive study of Paediatric Early Warning Systems and the first to deploy a functions-based approach to improvement in the UK with the aim to improve paediatric patient safety and reduce mortality. Our findings will inform recommendations about the safety processes for every hospital treating paediatric in-patients across the NHS. Sponsor: Cardiff University, 30-36 Newport Road, Cardiff, CF24 0DE Sponsor ref.: SPON1362-14. Funder: National Institute for Health Research, Health Services & Delivery Research Programme (NIHR HS&DR) Funder reference: 12/178/17. Research Ethics Committee reference: 15/SW/0084 [13/04/2015]. PROSPERO reference: CRD42015015326 [23/01/2015]. 94228292 https://doi.org/10.1186/ISRCTN94228292 [date of application 13/05/2015; date of registration: 18/08/2015]. Prospective registration prior to data collection and participant consent commencing in September 2014.

X Demographics

X Demographics

The data shown below were collected from the profiles of 34 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 124 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 13%
Student > Master 14 11%
Student > Ph. D. Student 8 6%
Student > Doctoral Student 8 6%
Unspecified 6 5%
Other 25 20%
Unknown 47 38%
Readers by discipline Count As %
Medicine and Dentistry 23 19%
Nursing and Health Professions 21 17%
Unspecified 6 5%
Social Sciences 5 4%
Psychology 5 4%
Other 15 12%
Unknown 49 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 November 2020.
All research outputs
#1,733,287
of 24,717,821 outputs
Outputs from BMC Pediatrics
#198
of 3,314 outputs
Outputs of similar age
#35,827
of 335,278 outputs
Outputs of similar age from BMC Pediatrics
#9
of 84 outputs
Altmetric has tracked 24,717,821 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,314 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 335,278 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.