Overdiagnosis means different things to different people. S M Carter and colleagues argue that we should use a broad term such as too much medicine for advocacy and develop precise, case by case definitions of overdiagnosis for research and clinical purposes

What ways of thinking and concrete strategies can assist qualitative health researchers to transition their research practice to online environments? We propose that researchers should foreground inclusion when designing online qualitative research, and suggest ethical, technological and social adaptations required to move data collection online. Existing research shows that this move can aid in meeting recruitment targets, but can also reduce the richness of the data generated, as well as how much participants enjoy participating, and the ability to achieve consensus in groups. Mindful and consultative choices are required to prevent these problems. To adapt to ethical challenges, researchers should especially consider participant privacy, and ways to build rapport and show appropriate care for participants, including protocols for dealing with distress or disengagement, managing data, and supporting consent. To adapt to technological challenges, research plans should choose between online modalities and platforms based on a clear understanding of their particular affordances and the implications of these. Finally, successful research in virtual social environments requires new protocols for engagement before data collection, attention to group numbers and dynamics, altered moderator teams and roles, and new logistical tasks for researchers. The increasing centrality of online environments to everyday life is driving traditional qualitative research methods to online environments and generating new qualitative research methods that respond to the particularities of online worlds. With strong design principles and attention to ethical, technical and social challenges, online methods can make a significant contribution to qualitative research in health.

‘One Health’ represents a call for health researchers and practitioners at the human, animal and environmental interfaces to work together to mitigate the risks of emerging and re-emerging infectious diseases (EIDs). A One Health approach emphasizing inter-disciplinary co-operation is increasingly seen as necessary for effective EID control and prevention. There are, however, socio-political, ethical and legal challenges, which must be met by such a One Health approach. Based on the philosophical review and critical analysis of scholarship around the theory and practice of One Health it is clear that EID events are not simply about pathogens jumping species barriers; they are comprised of complex and contingent sets of relations that involve socioeconomic and socio-political drivers and consequences with the latter extending beyond the impact of the disease. Therefore, the effectiveness of policies based on One Health depends on their implementation and alignment with or modification of public values. Despite its strong motivating rationale, implementing a One Health approach in an integrated and considered manner can be challenging, especially in the face of a perceived crisis. The effective control and prevention of EIDs therefore requires: (i) social science research to improve understanding of how EID threats and responses play out; (ii) the development of an analytic framework that catalogues case experiences with EIDs, reflects their dynamic nature and promotes inter-sectoral collaboration and knowledge synthesis; (iii) genuine public engagement processes that promote transparency, education and capture people’s preferences; (iv) a set of practical principles and values that integrate ethics into decision-making procedures, against which policies and public health responses can be assessed; (v) integration of the analytic framework and the statement of principles and values outlined above; and (vi) a focus on genuine reform rather than rhetoric.

Background There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race). Objectives Our objectives are to canvas the range of strategies stakeholders endorse in attempting to mitigate algorithmic bias, and to consider the ethical question of responsibility for algorithmic bias. Methodology The study involves in-depth, semistructured interviews with healthcare workers, screening programme managers, consumer health representatives, regulators, data scientists and developers. Results Findings reveal considerable divergent views on three key issues. First, views on whether bias is a problem in healthcare AI varied, with most participants agreeing bias is a problem (which we call the bias-critical view), a small number believing the opposite (the bias-denial view), and some arguing that the benefits of AI outweigh any harms or wrongs arising from the bias problem (the bias-apologist view). Second, there was a disagreement on the strategies to mitigate bias, and who is responsible for such strategies. Finally, there were divergent views on whether to include or exclude sociocultural identifiers (eg, race, ethnicity or gender-diverse identities) in the development of AI as a way to mitigate bias. Conclusion/significance Based on the views of participants, we set out responses that stakeholders might pursue, including greater interdisciplinary collaboration, tailored stakeholder engagement activities, empirical studies to understand algorithmic bias and strategies to modify dominant approaches in AI development such as the use of participatory methods, and increased diversity and inclusion in research teams and research participant recruitment and selection.