Background

BackgroundImplementing new practices requires changes in the behaviour of relevant actors, and this is facilitated by understanding of the determinants of current and desired behaviours. The Theoretical Domains Framework (TDF) was developed by a collaboration of behavioural scientists and implementation researchers who identified theories relevant to implementation and grouped constructs from these theories into domains. The collaboration aimed to provide a comprehensive, theory-informed approach to identify determinants of behaviour. The first version was published in 2005, and a subsequent version following a validation exercise was published in 2012. This guide offers practical guidance for those who wish to apply the TDF to assess implementation problems and support intervention design. It presents a brief rationale for using a theoretical approach to investigate and address implementation problems, summarises the TDF and its development, and describes how to apply the TDF to achieve implementation objectives. Examples from the implementation research literature are presented to illustrate relevant methods and practical considerations.MethodsResearchers from Canada, the UK and Australia attended a 3-day meeting in December 2012 to build an international collaboration among researchers and decision-makers interested in the advancing use of the TDF. The participants were experienced in using the TDF to assess implementation problems, design interventions, and/or understand change processes. This guide is an output of the meeting and also draws on the authors’ collective experience. Examples from the implementation research literature judged by authors to be representative of specific applications of the TDF are included in this guide.ResultsWe explain and illustrate methods, with a focus on qualitative approaches, for selecting and specifying target behaviours key to implementation, selecting the study design, deciding the sampling strategy, developing study materials, collecting and analysing data, and reporting findings of TDF-based studies. Areas for development include methods for triangulating data, e.g. from interviews, questionnaires and observation and methods for designing interventions based on TDF-based problem analysis.ConclusionsWe offer this guide to the implementation community to assist in the application of the TDF to achieve implementation objectives. Benefits of using the TDF include the provision of a theoretical basis for implementation studies, good coverage of potential reasons for slow diffusion of evidence into practice and a method for progressing from theory-based investigation to intervention.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-017-0605-9) contains supplementary material, which is available to authorized users.

BackgroundIt is increasingly acknowledged that ‘acceptability’ should be considered when designing, evaluating and implementing healthcare interventions. However, the published literature offers little guidance on how to define or assess acceptability. The purpose of this study was to develop a multi-construct theoretical framework of acceptability of healthcare interventions that can be applied to assess prospective (i.e. anticipated) and retrospective (i.e. experienced) acceptability from the perspective of intervention delivers and recipients.MethodsTwo methods were used to select the component constructs of acceptability. 1) An overview of reviews was conducted to identify systematic reviews that claim to define, theorise or measure acceptability of healthcare interventions. 2) Principles of inductive and deductive reasoning were applied to theorise the concept of acceptability and develop a theoretical framework. Steps included (1) defining acceptability; (2) describing its properties and scope and (3) identifying component constructs and empirical indicators.ResultsFrom the 43 reviews included in the overview, none explicitly theorised or defined acceptability. Measures used to assess acceptability focused on behaviour (e.g. dropout rates) (23 reviews), affect (i.e. feelings) (5 reviews), cognition (i.e. perceptions) (7 reviews) or a combination of these (8 reviews).From the methods described above we propose a definition: Acceptability is a multi-faceted construct that reflects the extent to which people delivering or receiving a healthcare intervention consider it to be appropriate, based on anticipated or experienced cognitive and emotional responses to the intervention. The theoretical framework of acceptability (TFA) consists of seven component constructs: affective attitude, burden, perceived effectiveness, ethicality, intervention coherence, opportunity costs, and self-efficacy.ConclusionDespite frequent claims that healthcare interventions have assessed acceptability, it is evident that acceptability research could be more robust. The proposed definition of acceptability and the TFA can inform assessment tools and evaluations of the acceptability of new or existing interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2031-8) contains supplementary material, which is available to authorized users.

, J. M. (2010). What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychology & Health, 25(10), pp. 1229-1245. doi: 10.1080 This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent 2What is an adequate sample size? Operationalising data saturation for theory-based interview studies AbstractIn interview studies, sample size is often justified by interviewing participants until reaching "data saturation". However, there is no agreed method of establishing this. We propose principles for deciding saturation in theory-based interview studies (where conceptual categories are pre-established by existing theory). First, specify a minimum sample size for initial analysis (initial analysis sample). Second, specify how many more interviews will be conducted without new ideas emerging (stopping criterion). We demonstrate these principles in two studies, based on Theory of Planned Behaviour, designed to identify three belief categories (Behavioural, Normative, Control), using an initial analysis sample of 10 and stopping criterion of 3. Study 1 (retrospective analysis of existing data) identified 84 shared beliefs of 14 general medical practitioners about managing patients with sore throat without prescribing antibiotics. The criterion for saturation was achieved for Normative beliefs but not for other beliefs or study-wise saturation. In Study 2 (prospective analysis), 17 relatives of people with Paget's disease of the bone reported 44 shared beliefs about taking genetic testing. Study-wise data saturation was achieved at interview 17. We propose specification of these principles for reporting data saturation in theory-based interview studies. The principles may be adaptable for other types of studies.