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'Multivoicedness' and the 'multivoiced Self' have become important theoretical concepts guiding research. Drawing on the tradition of dialogism, the Self is conceptualised as being constituted by a multiplicity of dynamic, interacting voices. Despite the growth in literature and empirical research, there remains a paucity of established methodological tools for analysing the multivoiced Self using qualitative data. In this article, we set out a systematic, practical 'how-to' guide for analysing multivoicedness. Using theoretically derived tools, our three-step method comprises: identifying the voices of I-positions within the Self's talk (or text), identifying the voices of 'inner-Others', and examining the dialogue and relationships between the different voices. We elaborate each step and illustrate our method using examples from a published paper in which data were analysed using this method. We conclude by offering more general principles for the use of the method and discussing potential applications.

If identities are socially produced, what happens when individuals grow up participating in divergent or conflicting social contexts? This paper reports upon research with second generation Turkish adolescents in London. Using the concept of the dialogical self, the research examines the dialogical structure of these young Turks’ selves. The analysis is Bakhtinian and seeks to identify the different discourses through which these young Turks talk about themselves. Three distinct discourses, or I-positions, are identified. These are then related to the sociocultural context within which these youth live, and specific attention is given to the constraints upon these youth in expressing aspects of their identity. We demonstrate that the asymmetries and tensions within these adolescents’ dialogical selves are adaptive to the fractured and asymmetrical social contexts in which they are embedded.

BACKGROUND: Access to safe surgical care represents a critical gap in healthcare delivery and development in many low- and middle-income countries, including Ethiopia. Quality improvement (QI) initiatives at hospital level may contribute to closing this gap. Many such quality improvement initiatives are carried out through international health partnerships. Better understanding of how to optimise quality improvement in low-income settings is needed, including through partnership-based approaches. Drawing on a process evaluation of an intervention to improve surgical services in an Ethiopian hospital, this paper offers lessons to help meet this need. METHODS: We conducted a qualitative process evaluation of a quality improvement project which aimed to improve access to surgical services in an Ethiopian referral hospital through better management. Data was collected longitudinally and included: 66 in-depth interviews with surgical staff and project team members; observation (135 h) in the surgery department and of project meetings; project-related documentation. Thematic analysis, guided by theoretical constructs, focused on identifying obstacles to implementation. RESULTS: The project largely failed to achieve its goals. Key barriers related to project design, partnership working and the implementation context, and included: confusion over project objectives and project and partner roles and responsibilities; logistical challenges concerning overseas visits; difficulties in communication; gaps between the time and authority team members had and that needed to implement and engage other staff; limited strategies for addressing adaptive-as opposed to technical-challenges; effects of hierarchy and resource scarcity on QI efforts. While many of the obstacles identified are common to diverse settings, our findings highlight ways in which some features of low-income country contexts amplify these common challenges. CONCLUSION: We identify lessons for optimising the design and planning of quality improvement interventions within such challenging healthcare contexts, with specific reference to international partnership-based approaches. These include: the need for a funded lead-in phase to clarify and agree goals, roles, mutual expectations and communication strategies; explicitly incorporating adaptive, as well as technical, solutions; transparent management of resources and opportunities; leadership which takes account of both formal and informal power structures; and articulating links between project goals and wider organisational interests.

Background: Large-scale data collection is an increasingly prominent and influential feature of efforts to improve healthcare delivery, yet securing the involvement of clinical centres and ensuring data comprehensiveness often proves problematic. We explore how improvements in both data submission and completion rates were achieved during a crucial period of the evolution of two large-scale data exercises. Methods: As part of an evaluation of a quality improvement programme, we conducted an ethnographic study involving 90 interviews and 47 days of non-participant observation of two UK national clinical audits in a period before submission of data on adherence to clinical standards became mandatory. Results: Critical to the improvements in submission and completion rates in the two exercises were the efforts of clinical leaders to refigure “data work” as a professionalization strategy. Using a series of strategic manoeuvres, leaders constructed a cultural account that tied the fortunes of the healthcare professions to the submission of high-quality data, proposing that it would demonstrate responsibility, transparency, and alignment with the public interest. In so doing, clinical leadership deployed tactics that might have been seen as unwarranted managerial aggression had they been imposed by parties external to the profession. Many residual challenges were linked not to principled objection by clinicians, but to mundane problems and frustrations in obtaining, recording, and submitting data. The cultural framing of data work as a professional duty was important to resolving its status as an abject form of labour. Conclusions: Improving data quality in large-scale exercises is possible, but requires cooperation with clinical centres. Enabling professional leadership of data work may offer some significant advantages, but attention is also needed to mundane and highly consequential obstacles to participation in data collection.