COVID-19 Public Health Measures and Patient and Public Involvement in Health and Social Care Research: An Umbrella Review.
Negin FouladiNedelina TchangalovaDamilola AjayiElizabeth MillweeCorinne LovettAlana Del SordiSamantha LiggettMalki De SilvaLaura BonillaAngel NkwontaLeah RamnarineAllyssa MunozKate FrazerPatrick GibbonsPublished in: International journal of environmental research and public health (2023)
An umbrella review of previously published systematic reviews was conducted to determine the nature and extent of the patient and public involvement (PPI) in COVID-19 health and social care research and identify how PPI has been used to develop public health measures (PHM). In recent years, there has been a growing emphasis on PPI in research as it offers alternative perspectives and insight into the needs of healthcare users to improve the quality and relevance of research. In January 2022, nine databases were searched from 2020-2022, and records were filtered to identify peer-reviewed articles published in English. From a total of 1437 unique records, 54 full-text articles were initially evaluated, and six articles met the inclusion criteria. The included studies suggest that PHM should be attuned to communities within a sociocultural context. Based on the evidence included, it is evident that PPI in COVID-19-related research is varied. The existing evidence includes written feedback, conversations with stakeholders, and working groups/task forces. An inconsistent evidence base exists in the application and use of PPI in PHM. Successful mitigation efforts must be community specific while making PPI an integral component of shared decision-making.
Keyphrases
- healthcare
- public health
- protein protein
- coronavirus disease
- sars cov
- meta analyses
- mental health
- small molecule
- case report
- quality improvement
- systematic review
- health information
- palliative care
- emergency department
- tyrosine kinase
- climate change
- randomized controlled trial
- respiratory syndrome coronavirus
- magnetic resonance imaging
- big data
- computed tomography
- smoking cessation
- chronic pain
- deep learning