Support after COVID-19 study: a mixed-methods cross-sectional study to develop recommendations for practice.

COVID-19 HEALTH SERVICES ADMINISTRATION & MANAGEMENT Health policy REHABILITATION MEDICINE SOCIAL MEDICINE

Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
29 08 2022
Historique:
entrez: 29 8 2022
pubmed: 30 8 2022
medline: 1 9 2022
Statut: epublish

Résumé

Objectives of study stage 1 were to: explore people's experiences of illness due to COVID-19 while feeling socially isolated or socially isolating; identify perceptions of what would support recovery; and synthesise insights into recommendations for supporting people after COVID-19. Study stage 2 objectives were to engage stakeholders in evaluating these recommendations and analyse likely influences on access to the support identified. A two-stage, multimethod cross-sectional study was conducted from a postpositivist perspective. Stage 1 included an international online survey of people's experiences of illness, particularly COVID-19, in isolation (n=675 full responses). Stage 2 involved a further online survey (n=43), two tweetchats treated as large online focus groups (n=60 and n=27 people tweeting), two smaller focus groups (both n=4) and one interview (both using MS teams). Stage 1 had an international emphasis, although 87% of respondents were living in the UK. Stage 2 focused on the UK. Anyone aged 18+ and able to complete a survey in English could participate. Stage 2 included health professionals, advocates and people with lived experience. Descriptive data and response categories derived from open responses to the survey and the qualitative data. Of those responding fully to stage 1 (mean age 44 years); 130 (19%) had experienced COVID-19 in isolation; 45 had recovered, taking a mean of 5.3 (range 1-54) weeks. 85 did not feel they had recovered; fatigue and varied 'other' symptoms were most prevalent and also had most substantial negative impacts. Our draft recommendations were highly supported by respondents to stage 2 and refined to produce final recommendations. Recommendations support access to progressive intensity and specialism of support, addressing access barriers that might inadvertently increase health inequalities. Multidisciplinary collaboration and learning are crucial, including the person with COVID-19 and/or Long Covid in the planning and decision making throughout.

Identifiants

pubmed: 36038169
pii: bmjopen-2021-056568
doi: 10.1136/bmjopen-2021-056568
pmc: PMC9437739
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e056568

Subventions

Organisme : Chief Scientist Office
ID : COV/QMU/20/04
Pays : United Kingdom

Informations de copyright

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

Références

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Auteurs

Cathy Bulley (C)

School of Health Sciences, Queen Margaret University Edinburgh, Musselburgh, UK CBulley@qmu.ac.uk.

Vaibhav Tyagi (V)

Nursing, Queen Margaret University Edinburgh, Musselburgh, UK.

Eleanor Curnow (E)

School of Health Sciences, Queen Margaret University Edinburgh, Musselburgh, UK.

Kath Nicol (K)

Physiotherapy, Queen Margaret University Edinburgh, Musselburgh, UK.

Lisa Salisbury (L)

Physiotherapy, Queen Margaret University Edinburgh, Musselburgh, UK.

Kim Stuart (K)

Research and Knowledge Exchange Development Unit, Queen Margaret University Edinburgh, Musselburgh, UK.

Brendan McCormack (B)

Centre for Person-centred Practice Research, Queen Margaret University Edinburgh, Musselburgh, UK.

Ruth Magowan (R)

Nursing, Queen Margaret University Edinburgh, Musselburgh, UK.

Olivia Sagan (O)

School of Health Sciences, Queen Margaret University Edinburgh, Musselburgh, UK.

Jan Dewing (J)

Centre for Person-centred Practice Research, Queen Margaret University Edinburgh, Musselburgh, UK.

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Classifications MeSH