Optimising cultural provision to improve older people’s wellbeing through social prescribing in the context of COVID-19: what works, for whom, in what circumstances, and why?
As this is a rapid realist review, with a time limited duration, the search for literature will be purposive rather than exhaustive (Pawson et al., 2005).
The research team has extensive knowledge of literature on a) social prescribing, and b) the cultural sector’s role in supporting public well-being; hence, our search strategy will be informed by our team’s content expertise.
We will receive support from an information specialist (Roberts) to assist with constructing searches on two electronic databases - CINAHL and EMBASE.
We will also look for references from those listed in papers that we include in the review.
In order to locate additional grey literature, we will conduct a keyword search on Google, looking at the first 10 pages for relevant references.
We will limit searches to the year 2000 onwards. Our previous reviews and our consensus expertise has suggested that this is a timepoint from which most relevant literature on social prescribing was published.
We will also only include literature written in English.
The search will evolve as understanding of the topic advances. Searching in realist reviews is iterative because “as the synthesis progresses, new or refined elements of theory may be required to explain particular findings, or to examine specific aspects of particular processes” (Wong et al., 2013: 8). Searching will not be exhaustive; it will continue until data are sufficient to support a clear and credible final programme theory.
Types of study to be included
A range of literature will be considered, including quantitative, qualitative and mixed methods research, existing evidence reviews, policy documents, evaluations, reports and blogs.
Condition or domain being studied
The role of the cultural sector in supporting personal wellbeing.
Older people (aged ≥ 70 years) as this was the age group defined as particularly ‘at risk’ from the pandemic.
Social prescribing and the cultural sector’s potential role in this activity within the context of COVID-19. By social prescribing we mean the direction of patients to non‐medical, community or social activities to help them manage and prevent illness and improve their health and wellbeing (Husk et al., 2020).
To develop a programme theory that explains mechanisms, contexts and circumstances associated with outcomes from the cultural sector playing a role in social prescribing for older people within the context of COVID-19.
* Measures of effect
* Measures of effect
Data extraction (selection and coding)
References will be screened for inclusion by title and abstract, and if further information is required, full texts will be retrieved and read.
Documents will be included based on their contribution to theory building and/or testing - i.e. relevance. A preliminary set of inclusion/exclusion criteria will be piloted and revised by two reviewers, who together will assess and discuss a random sample of 10% of references. The remaining 90% will be screened by a single reviewer, although it is anticipated that several of the documents will be read as full texts by members of the team if they are regarded as pivotal or it is unclear how they might add to addressing the review question.
Rayaan will be used to record decisions on whether or not documents are included.
Documents will be sorted into broad ‘groupings’ based on topic (e.g. cultural provision of focus such as museums, parks, libraries), which will assist with selecting papers purposively for informing specific parts of the programme theory.
Included documents will be downloaded into NVivo, and sections of data judged to be relevant, referring to contexts, mechanisms or outcomes and/or the relationships between these, will be coded.
These data will assist in the development and testing of context-mechanism-outcome configurations. Deductive coding (directed by an initial programme theory that we create at the start of the review from a preliminary literature search) and inductive coding (created whilst reading papers) will be performed. Key characteristics of documents will be entered into an Excel file. These will be used to help interpret the nature of the data sources and thus the strength of the inferences of context-mechanism-outcome configurations.
Data extraction will be performed by one reviewer and 10% of extracted data will be reviewed independently by another member of the team.
Risk of bias (quality) assessment
Papers will be evaluated as being conceptually rich or thin based on previous approaches to doing this (Ritzer, 1991; Roen et al., 2006), allowing reviewers to initially focus data extraction and analysis on conceptually richer papers, whilst not excluding those that may be weaker but could still make an important contribution to the final programme theory.
The analysis will start by looking at studies conducted in the UK and those we consider most relevant (being given a score of 3 for relevance by reviewers when appraising papers):
• 3 = a highly relevant study, focused on exploring/understanding wellbeing for older people and the cultural sector that makes reference to social prescribing; contains significant amounts of highly relevant information/quotations;
• 2 = a relevant study, which whilst not centred on exploring/understanding wellbeing for older people and the cultural sector as part of social prescribing, contains a significant amount of relevant information/quotations;
• 1 = a study with limited relevance that only mentions briefly wellbeing for older people from cultural sector engagement and/or its role in social prescribing.
Strategy for data synthesis
A realist logic of analysis will be used to integrate data into a final programme theory, to understand potential outcomes associated with the cultural sector’s role in social prescribing for older people; exploring in particular, for whom these outcomes occur, in what circumstances and why.
Data from included documents will be interpreted as referring to context, mechanism or outcome and/or the relationships between these, thus enabling the building of context-mechanism-outcome configurations. These data will then be interpreted to understand the relationships of these context-mechanism-outcome configurations with the initial programme theory, which will be revised accordingly. Cross-case comparisons of data will be employed to comprehend why specific outcomes might occur or not, depending on context and mechanisms.
To assist the synthesis, we will use one or more of the following analytic processes (Pawson, 2006):
• Juxtaposition: data on mechanisms in one document shed light on patterns referenced in another;
• Reconciliation of discrepancies: contextual factors that explain differences in outcomes;
• Adjudication: issues around methodological quality is used to judge the plausibility between data used to support different context-mechanism-outcome-configurations;
• Consolidation: building arguments on why outcomes differ in particular contexts drawing on data from more than one document;
• Situation: describing settings to explain how mechanisms are activated in specific contexts.
The final programme theory will be presented in the form of a narrative, which will include text, tables, figures and information of the context-mechanism-outcome configurations developed and their relationship within the programme theory. When writing up, we will follow the Realist and Meta-Review Evidence Synthesis: Evolving Standards (RAMESES) guidelines (Wong et al., 2013). It is anticipated that results will provide information that is useful to cultural sector providers as well as those involved in delivering or commissioning social prescribing.
Analysis of subgroups or subsets
Contact details for further information
Organisational affiliation of the review
Centre for Evidence Based Medicine, University of Oxford
Review team members and their organisational affiliations
Dr Stephanie Tierney. University of Oxford
Assistant/Associate Professor Geoff Wong. University of Oxford
Amadea Turk. University of Oxford
Lucy Shaw. University of Oxford
Professor Helen Chatterjee. University College London
Dr Kerryn Husk. University of Plymouth
Dr Kathryn Eccles. University of Oxford
Dr Caroline Potter. University of Oxford
Beth McDougall. University of Oxford
Dr Harriet Warburton. University of Oxford
Dr Emma Webster. University of Oxford
Nia Roberts. University of Oxford
Assistant/Associate Professor Kamal Mahtani. University of Oxford
Type and method of review
Epidemiologic, Intervention, Service delivery, Systematic review, Other
Anticipated or actual start date
01 October 2020
Anticipated completion date
01 March 2021
UKRI Arts and Humanities Research Council (AHRC)
State the funder, grant or award number and the date of award
AH/V008781/1 Awarded Sept 2020
Conflicts of interest
Stage of review
Subject index terms status
Subject indexing assigned by CRD
Subject index terms
Aged; Aged, 80 and over; Coronavirus; Coronavirus Infections; COVID-19; Cultural Competency; Culturally Competent Care; Delivery of Health Care; Healthcare Disparities; Health Status Disparities; Humans; Pandemics; Public Health; Quality of Life; Social Determinants of Health; Social Planning
Date of registration in PROSPERO
23 September 2020
Date of first submission
23 September 2020
Stage of review at time of this submission
|Piloting of the study selection process||No||No|
|Formal screening of search results against eligibility criteria||No||No|
|Risk of bias (quality) assessment||No||No|
The record owner confirms that the information they have supplied for this submission is accurate and complete and they understand that deliberate provision of inaccurate information or omission of data may be construed as scientific misconduct.
The record owner confirms that they will update the status of the review when it is completed and will add publication details in due course.