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Medical Education 2012: 46: 372-380 Context Text is familiar to us all. This paper offers an introduction to, and an exploration of, the range of methodological possibilities open to the education researcher who has chosen to use text as a research data source. It encourages a purposeful deliberation of the different textual sources available as data, the range of methodological approaches possible and the types of interpretation that can be adopted when embarking on an empirical study using textual data. Methods Approaches to interpreting text are varied and utilise a range of analytical and interpretative strategies. To illustrate the theoretical points raised within this paper, two contrasting methods were applied to the same text. Tag cloud analysis and performative narrative analysis (PNA) were employed to analyse Chapter 4 of the UK government's 2010 White Paper Equity and excellence: Liberating the NHS. Results The adoption of these contrasting methodologies, which are not currently used extensively in medical education research, revealed that some common issues were identified by both tag clouds and PNA, but, in addition, each approach was able to unveil something unique about the text. Conclusions These two methods highlight the range of affordances, or possibilities, that textual analysis will have on the results. We suggest that medical education researchers should be encouraged to move away from the current dominant and privileged methodologies that seek to provide answers and explore other methods and approaches to textual data that encourage us to question and reflect more deeply. [ABSTRACT FROM AUTHOR]

Context: Within the social sciences, researchers increasingly build on visual methods to explore complex phenomena and understand how people experience and give meaning to this complexity. Amongst the variety of visual methods available, rich pictures are beginning to gain traction in health professions education (HPE) research. Approach: A rich picture is a pictorial representation of a particular situation, including what happened, who was involved, how people felt, how people acted, how people behaved, and what external pressures they acted upon. Rich pictures expand our perspective; they may highlight connections, illuminate the big picture and reveal unexpected emotions. Although new methods bring excitement to the field, it is our responsibility to also be cautious and insightful about their limitations. Rich pictures are a method in evolution in HPE research, with many unknowns about what is possible and what is optimal. Purpose: In the current paper, we aim to map out the background, describe the process and share some reflective insights of using rich pictures as a data collection method. Cristancho and Helmich explain how rich pictures can be used to explore complexity in Health Professions Education, advancing the field's methodological options by highlighting their advantages and inherent limitations. [ABSTRACT FROM AUTHOR]

Background: Qualitative research approaches are increasingly integrated into medical education research to answer relevant questions that quantitative methodologies cannot accommodate. However, researchers have found that traditional qualitative methodological approaches reflect the foundations and objectives of disciplines whose aims are recognizably different from the medical education domain of inquiry (Thorne, 2016, Interpretive description. New York, NY: Routledge). Interpretive description (ID), a widely used qualitative research method within nursing, offers an accessible and theoretically flexible approach to analysing qualitative data within medical education research. ID is an appropriate methodological alternative for medical education research, as it can address complex experiential questions while producing practical outcomes. It allows for the advancement of knowledge surrounding educational experience without sacrificing methodological integrity that long‐established qualitative approaches provide. Purpose: In this paper, we present interpretive description as a useful research methodology for qualitative approaches within medical education. We then provide a toolkit for medical education researchers interested in incorporating interpretive description into their study design. We propose a coherent set of strategies for identifying analytical frameworks, sampling, data collection, analysis, rigour and the limitations of ID for medical education research. We conclude by advocating for the interpretive description approach as a viable and flexible methodology for medical education research. In the latest "Research Approaches" article, Thompson‐Burdine et al. introduce us to Interpretive Description as a means for addressing complex experiential questions while producing practical outcomes. [ABSTRACT FROM AUTHOR]

Context Qualitative, social science approaches to research have surged in popularity within health professions education ( HPE) over the past decade. Institutional ethnography ( IE) offers the field another sociological approach to inquiry. Although widely used in nursing and health care research, IE remains relatively uncommon in the HPE research community. This article provides a brief introduction to IE and suggests why HPE researchers may wish to consider it for future studies. Methods Part 1 of this paper presents IE's conceptual grounding in: (i) the entry point to inquiry ('materiality'), (ii) a generous definition of 'work' and (iii) a focus on how 'texts' such as policies, forms and written protocols influence activity. Part 2 of this paper outlines the method's key features through exemplars from our own research. Part 3 discusses the ways in which research that blurs the lines between educational and clinical practice can be both generative for HPE and accomplished using IE. Results The authors demonstrate the usefulness of IE for studying complex social issues in HPE. It is posited that a key added value of IE is that it goes beyond individual-level explanations of problems and phenomena, yet also closely studies individuals' activities, rather than remaining at an abstract or distant level of analysis. Thereby, IE can result in feasible and meaningful social change at the nexus of health professions education and other social systems such as clinical practice. Conclusions IE adds to the growing qualitative research toolkit for HPE researchers. It is worth considering because it may enable change through the study of HPE in relation to other social processes, structures and systems, including the clinical practice world. A particular benefit may be found in blending HPE research with research on clinical practice, toward changing practice and policy through IE, given the interrelated nature of these fields. [ABSTRACT FROM AUTHOR]

Context: The electronic health record (EHR) has been identified as a potential site for gathering data about trainees' clinical performance, but these data are not collected or organised for this purpose. Therefore, a careful and rigorous approach is required to explore how EHR data could be meaningfully used for assessment purposes. The purpose of this study was to identify EHR performance metrics that represent both the independent and interdependent clinical performance of emergency medicine (EM) trainees and explore how they might be meaningfully used for assessment and feedback. Methods: Using constructivist grounded theory, we conducted 21 semi‐structured interviews with EM faculty members and residents. Participants were asked to identify the clinical actions of trainees that would be valuable for assessment and feedback and describe how those activities are represented in the EHR. Data collection and analysis, which consisted of three stages of coding, occurred iteratively. Results: When faculty members and trainees in EM were asked to reflect on the usefulness of using EHR performance metrics for resident assessment and feedback they expressed both widespread support for the idea in principle and hesitation that aspects of clinical performance captured in the data would not be representative of residents' individual performance, but would rather reflect their interdependence with other team members and the systems in which they work. We highlight three categorisations of system‐level interdependence ‐ medical directives, technological systems and organisational systems ‐ identified by our participants, and discuss strategies participants employed to navigate these forms of interdependence within the health care system. Conclusions: System‐level interdependence shapes physicians' performances, and yet, this impact is rarely corrected for or noted within clinical performance data. Educators have a responsibility to recognise system‐level interdependence when teaching and consider system‐level interdependence when assessing the performance of trainees in order to most effectively and fairly utilise the EHR as a source of assessment data. In this paper, the authors highlight factors that contribute to system‐level interdependence and discuss the implications these factors have for resident assessment. [ABSTRACT FROM AUTHOR]

Context Autoethnography is a methodology that allows clinician-educators to research their own cultures, sharing insights about their own teaching and learning journeys in ways that will resonate with others. There are few examples of autoethnographic research in medical education, and many areas would benefit from this methodology to help improve understanding of, for example, teacher-learner interactions, transitions and interprofessional development. Objectives We wish to share this methodology so that others may consider it in their own education environments as a viable qualitative research approach to gain new insights and understandings. Methods This paper introduces autoethnography, discusses important considerations in terms of data collection and analysis, explores ethical aspects of writing about others and considers the benefits and limitations of conducting research that includes self. Results Autoethnography allows medical educators to increasingly engage in self-reflective narration while analysing their own cultural biographies. It moves beyond simple autobiography through the inclusion of other voices and the analytical examination of the relationships between self and others. Autoethnography has achieved its goal if it results in new insights and improvements in personal teaching practices, and if it promotes broader reflection amongst readers about their own teaching and learning environments. Conclusions Researchers should consider autoethnography as an important methodology to help advance our understanding of the culture and practices of medical education. [ABSTRACT FROM AUTHOR]

Content Humans are fundamentally social beings. The social systems within which we live our lives (families, schools, workplaces, professions, friendship groups) have a significant influence on our health, success and well-being. These groups can be characterised as networks and analysed using social network analysis. Social Network Analysis Social network analysis is a mainly quantitative method for analysing how relationships between individuals form and affect those individuals, but also how individual relationships build up into wider social structures that influence outcomes at a group level. Recent increases in computational power have increased the accessibility of social network analysis methods for application to medical education research. Application to Medical Education Social network analysis has been used to explore team-working, social influences on attitudes and behaviours, the influence of social position on individual success, and the relationship between social cohesion and power. This makes social network analysis theories and methods relevant to understanding the social processes underlying academic performance, workplace learning and policy-making and implementation in medical education contexts. Conclusions Social network analysis is underused in medical education, yet it is a method that could yield significant insights that would improve experiences and outcomes for medical trainees and educators, and ultimately for patients. [ABSTRACT FROM AUTHOR]

Context Visual expertise is the superior visual skill shown when executing domain-specific visual tasks. Understanding visual expertise is important in order to understand how the interpretation of medical images may be best learned and taught. In the context of this article, we focus on the visual skill of medical image diagnosis and, more specifically, on the methodological set-ups routinely used in visual expertise research. Methods We offer a critique of commonly used methods and propose three challenges for future research to open up new avenues for studying characteristics of visual expertise in medical image diagnosis. The first challenge addresses theory development. Novel prospects in modelling visual expertise can emerge when we reflect on cognitive and socio-cultural epistemologies in visual expertise research, when we engage in statistical validations of existing theoretical assumptions and when we include social and socio-cultural processes in expertise development. The second challenge addresses the recording and analysis of longitudinal data. If we assume that the development of expertise is a long-term phenomenon, then it follows that future research can engage in advanced statistical modelling of longitudinal expertise data that extends the routine use of cross-sectional material through, for example, animations and dynamic visualisations of developmental data. The third challenge addresses the combination of methods. Alternatives to current practices can integrate qualitative and quantitative approaches in mixed-method designs, embrace relevant yet underused data sources and understand the need for multidisciplinary research teams. Conclusion Embracing alternative epistemological and methodological approaches for studying visual expertise can lead to a more balanced and robust future for understanding superior visual skills in medical image diagnosis as well as other medical fields. [ABSTRACT FROM AUTHOR]

Medical Education 2012: 46: 943-952 Objectives As medical education research continues to proliferate, evidence syntheses will become increasingly important. The purpose of this article is to provide a concise and practical guide to the conduct and reporting of systematic reviews. Results (i) Define a focused question addressing the population, intervention, comparison (if any) and outcomes. (ii) Evaluate whether a systematic review is appropriate to answer the question. Systematic and non-systematic approaches are complementary; the former summarise research on focused topics and highlight strengths and weaknesses in existing bodies of evidence, whereas the latter integrate research from diverse fields and identify new insights. (iii) Assemble a team and write a study protocol. (iv) Search for eligible studies using multiple databases (MEDLINE alone is insufficient) and other resources (article reference lists, author files, content experts). Expert assistance is helpful. (v) Decide on the inclusion or exclusion of each identified study, ideally in duplicate, using explicitly defined criteria. (vi) Abstract key information (including on study design, participants, intervention and comparison features, and outcomes) for each included article, ideally in duplicate. (vii) Analyse and synthesise the results by narrative or quantitative pooling, investigating heterogeneity, and exploring the validity and assumptions of the review itself. In addition to the seven key steps, the authors provide, information on electronic tools to facilitate the review process, practical tips to facilitate the reporting process and an annotated bibliography. [ABSTRACT FROM AUTHOR]

The article presents abstracts of articles related to medical education including "Working with words: exploring textual analysis in medical education research," by D. Gill, A. Griffin, and S. Park, "From test validity to construct validityand back?," by S. Verhulst, M. Conlee, and J. Colliver, and "How lead consultants approach educational change in postgraduate medical education," by F. Scheele and colleagues.