“How PrEPared are you?”: Knowledge of and attitudes toward PrEP among overseas-born and newly arrived gay, bisexual, and other men who have sex with men in Australia

Qualitative research explores complex phenomena encountered by clinicians, health care providers, policy makers and consumers. Although partial checklists are available, no consolidated reporting framework exists for any type of qualitative design. To develop a checklist for explicit and comprehensive reporting of qualitative studies (in depth interviews and focus groups). We performed a comprehensive search in Cochrane and Campbell Protocols, Medline, CINAHL, systematic reviews of qualitative studies, author or reviewer guidelines of major medical journals and reference lists of relevant publications for existing checklists used to assess qualitative studies. Seventy-six items from 22 checklists were compiled into a comprehensive list. All items were grouped into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. Duplicate items and those that were ambiguous, too broadly defined and impractical to assess were removed. Items most frequently included in the checklists related to sampling method, setting for data collection, method of data collection, respondent validation of findings, method of recording data, description of the derivation of themes and inclusion of supporting quotations. We grouped all items into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. The criteria included in COREQ, a 32-item checklist, can help researchers to report important aspects of the research team, study methods, context of the study, findings, analysis and interpretations.

Background Purposive sampling has a long developmental history and there are as many views that it is simple and straightforward as there are about its complexity. The reason for purposive sampling is the better matching of the sample to the aims and objectives of the research, thus improving the rigour of the study and trustworthiness of the data and results. Four aspects to this concept have previously been described: credibility, transferability, dependability and confirmability. Aims The aim of this paper is to outline the nature and intent of purposive sampling, presenting three different case studies as examples of its application in different contexts. Results Presenting individual case studies has highlighted how purposive sampling can be integrated into varying contexts dependent on study design. The sampling strategies clearly situate each study in terms of trustworthiness for data collection and analysis. The selected approach to purposive sampling used in each case aligns to the research methodology, aims and objectives, thus addressing each of the aspects of rigour. Conclusions Making explicit the approach used for participant sampling provides improved methodological rigour as judged by the four aspects of trustworthiness. The cases presented provide a guide for novice researchers of how rigour may be addressed in qualitative research.

Health professions education (HPE) researchers are regularly asked to articulate their use of theory, theoretical frameworks, and conceptual frameworks in their research. However, all too often, these words are used interchangeably or without a clear understanding of the differences between these concepts. Further problematizing this situation is the fact that theory, theoretical framework, and conceptual framework are terms that are used in different ways in different research approaches. In this article, the authors set out to clarify the meaning of these terms and to describe how they are used in 2 approaches to research commonly used in HPE: the objectivist deductive approach (from theory to data) and the subjectivist inductive approach (from data to theory). In addition to this, given that within subjectivist inductive research theory, theoretical framework, and conceptual framework can be used in different ways, they describe 3 uses that HPE researchers frequently rely on: fully inductive theory development, fully theory-informed inductive, and theory-informing inductive data analysis.