Edward Duncan
A national survey of community rehabilitation service provision for people with long Covid in Scotland.
Duncan, Edward; Cooper, Kay; Cowie, Julie; Alexander, Lyndsay; Morris, Jacqui; Preston, Jenny
Authors
Professor Kay Cooper k.cooper@rgu.ac.uk
Associate Dean (Research)
Julie Cowie
Dr Lyndsay Alexander l.a.alexander@rgu.ac.uk
Associate Professor
Jacqui Morris
Jenny Preston
Abstract
Over 50 million cases of COVID-19 have been confirmed globally as of November 2020. Evidence is rapidly emerging on the epidemiology of COVID-19, and its impact on individuals and potential burden on health services and society. Between 10–35% of people with COVID-19 may experience post-acute long Covid. This currently equates to between 8,129 and 28,453 people in Scotland. Some of these people will require rehabilitation to support their recovery. Currently, we do not know how to optimally configure community rehabilitation services for people with long Covid. This national survey aimed to provide a detailed description of current community rehabilitation provision for people with long Covid in Scotland. We developed, piloted, and conducted a national electronic survey of current community rehabilitation service provision for people presenting with long Covid symptomatology. Our sample were the Allied Health Professions Directors of all 14 territorial NHS Health Boards in Scotland. Fixed response and narrative data were analysed descriptively. Responses were received from all respondents (14/14), enabling a national picture to be gained. Almost all Health Boards (13/14) currently deliver rehabilitation for people with long Covid within pre-existing services. Fatigue (11/14) and respiratory conditions (9/14) were the two most common presenting problems of patients. Most long Covid community rehabilitation services are delivered through a combination of face-to-face and digital contact (13/14). Community rehabilitation for people with long Covid is an emerging reality. This survey provides a national picture of current community rehabilitation for people with long Covid. We do not know how community rehabilitation can be optimally delivered for this population. This is vital as community rehabilitation services were already under pressure prior to the emergence of COVID-19. Further research is urgently required to investigate the implementation, outcomes and cost-effectiveness of differing models of community rehabilitation for this patient population.
Citation
DUNCAN, E., COOPER, K., COWIE, J., ALEXANDER, L., MORRIS, J. and PRESTON, J. 2021. A national survey of community rehabilitation service provision for people with long Covid in Scotland. F1000Research [online], 9, article number 1416, [version 2; peer review: 2 approved]. Available from: https://doi.org/10.12688/f1000research.27894.2
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 7, 2020 |
Online Publication Date | Mar 26, 2021 |
Publication Date | Mar 26, 2021 |
Publicly Available Date | May 13, 2021 |
Journal | F1000Research |
Electronic ISSN | 2046-1402 |
Publisher | F1000Research |
Peer Reviewed | Peer Reviewed |
Volume | 9 |
Article Number | 1416 |
DOI | https://doi.org/10.12688/f1000research.27894.2 |
Keywords | COVID-19; Long Covid; Community rehabilitation; Allied health professions; Survey; COVID-19 pandemic |
Public URL | https://rgu-repository.worktribe.com/output/1280515 |
Related Public URLs | https://rgu-repository.worktribe.com/output/1335425 (Dataset) |
Additional Information | This is a updated version, the first published version can be found at https://doi.org/10.12688/f1000research.27894.1. |
Files
DUNCAN 2021 A national survey (VOR v2)
(1.1 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Version
Updated 2024-03-04
You might also like
Large scoping reviews: managing volume and potential chaos in a pool of evidence sources.
(2024)
Journal Article
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search