Developing programme theories as part of a realist evaluation of a healthcare quality improvement programme

Feather, Julie

doi:10.1177/2053434518779753

Cited by 12 publications

(8 citation statements)

References 22 publications

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“…CMO coding also required that we made clear distinctions about what data belonged in each of these three categories. Similar to many others (e.g., [31,68,69]), we found this hard to do with certainty, especially in the initial stages of coding, but we did find it helpful to follow Westhorp's advice for identifying mechanisms:…”

Section: Task 4 Code Data To Capture Causal Relationshipssupporting

confidence: 62%

Using Realist Evaluation to Understand Process Outcomes in a COVID-19-Impacted Yoga Intervention Trial: A Worked Example

Haynes

Gilchrist

Oliveira

et al. 2021

IJERPH

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Realist evaluation offers a valuable way to understand how interventions function and thus how they can be improved and locally adapted. Consequently, realist evaluation is increasingly conducted in parallel with intervention trials. It comprises a clear philosophical foundation and view of causality, pragmatic mixed data collection methods, and a theory-driven approach in which hypothesised program theories are tested and refined. However, detailed methods for data analysis are seldom well-described in realist studies and no clear method for analysing and presenting realist evaluation data has yet emerged. In this methodological paper we use the worked example of our realist process evaluation of the SAGE yoga trial to illustrate an applied process of data analysis and presentation of findings. We show how we drew on other realist studies for ideas, provide examples of six key tasks involved in conducting a realist process evaluation (including coding data and structuring results) and describe strategies that did not work and our rationale for rejecting them. This detailed account of the decisions and methods that worked for us is intended to provide a practical and informed point of departure for researchers conducting a realist evaluation.

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Section: Task 4 Code Data To Capture Causal Relationshipssupporting

confidence: 62%

Using Realist Evaluation to Understand Process Outcomes in a COVID-19-Impacted Yoga Intervention Trial: A Worked Example

Haynes

Gilchrist

Oliveira

et al. 2021

IJERPH

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“…However, a multiple case study approach31 may have aided linkage of the COM-B domains to adherence with IMT facilitating the identification of barriers to IMT and triangulation. Further research may incorporate realist evaluation in order to illuminate the mechanisms of this complex intervention and the context of implementation 62 63…”

Section: Discussionmentioning

confidence: 99%

Is inspiratory muscle training (IMT) an acceptable treatment option for people with chronic obstructive pulmonary disease (COPD) who have declined pulmonary rehabilitation (PR) and can IMT enhance PR uptake? A single-group prepost feasibility study in a home-based setting

et al. 2019

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ObjectivesThis feasibility study aimed to assess the acceptability of inspiratory muscle training (IMT) in people with chronic obstructive pulmonary disease (COPD) who declined pulmonary rehabilitation (PR) as a potential treatment option or precursor to PR. Objectives were to assess attitudes to IMT, PR and alternatives to PR; factors influencing adherence with IMT and acceptability of outcome measures, research tools and study protocol.DesignA pragmatic, mixed methods, prepost feasibility study was conducted. Recruitment took place over a 4-month period. Participants were followed up for a period of 6 months.SettingsIMT sessions and assessments were conducted in the domiciliary setting.ParticipantsInclusion criteria: people over the age of 35, stable COPD, Medical Research Council Dyspnoea scale of 3 or above, declined PR. Exclusion criteria: history of spontaneous pneumothorax, incomplete recovery from a traumatic pneumothorax, asthma, known recently perforated eardrum, unstable angina, ventricular dysrhythmias, cerebrovascular event or myocardial infarction within the last 2 months. Participants were selected from a purposive sample. Of the 22 potential participants screened, 11 were recruited and interviewed. Ten participants commenced IMT. Seven participants completed the follow-up assessment.InterventionEight weeks of IMT twice a day, 5 days a week with visits once weekly by a physiotherapist. Unsupervised IMT twice a day three times a week until follow-up at 6 months.OutcomesAcceptability of IMT and the study process was explored via semi-structured interviews. Adherence with IMT was assessed by the Powerbreathe K3 device and participant diaries. Uptake of PR was identified.ResultsIMT was found to be acceptable. Adherence was explored. Four people went on to participate in PR.ConclusionsFeasibility was established. A randomised controlled trial is warranted to establish efficacy and cost-effectiveness of IMT in those who decline PR and IMT as an intervention to promote uptake of PR.Trial registration numberNCT01956565; Post-results.

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“…Realist evaluations, however, traditionally focus on the programme level. They are an exploratory device to make sense of programmes as a whole (Feather, 2018;Segal et al, 2011;Van Belle et al, 2017;Williams et al, 2017).…”

Section: Evaluating Care Integration Under the Conditions Of Big Datamentioning

confidence: 99%

Big Data and what it means for evaluating integrated care programmes

Kaehne

2019

JICA

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Purpose Big Data is likely to have significant implications for the way in which services are planned, organised or delivered as well as the way in which we evaluate them. The increase in data availability creates particular challenges for evaluators in the field of integrated care and the purpose of this paper is to set out how we may usefully reframe these challenges in the longer term. Design/methodology/approach Using the characteristics of Big Data as defined in the literature, the paper develops a narrative around the data and research design challenges and how they influence evaluation studies in the field of care integration. Findings Big Data will have significant implications for how we conduct integrated care evaluations. In particular, dynamic modelling and study designs capable of accommodating new epistemic foundations for the phenomena of social organisations, such as emergence and feedback loops, are likely to be most helpful. Big Data also generates opportunities for exploratory data analysis approaches, as opposed to static model development and testing. Evaluators may find research designs useful that champion realist approaches or single-n designs. Originality/value This paper reflects on the emerging literature and changing practice of data generation and data use in health care. It draws on organisational theory and outlines implications of Big Data for evaluating care integration initiatives.

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Developing programme theories as part of a realist evaluation of a healthcare quality improvement programme

Cited by 12 publications

References 22 publications

Using Realist Evaluation to Understand Process Outcomes in a COVID-19-Impacted Yoga Intervention Trial: A Worked Example

Using Realist Evaluation to Understand Process Outcomes in a COVID-19-Impacted Yoga Intervention Trial: A Worked Example

Is inspiratory muscle training (IMT) an acceptable treatment option for people with chronic obstructive pulmonary disease (COPD) who have declined pulmonary rehabilitation (PR) and can IMT enhance PR uptake? A single-group prepost feasibility study in a home-based setting

Big Data and what it means for evaluating integrated care programmes

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