In response to the COVID-19 pandemic, large-scale research and pharmaceutical regulatory processes have proceeded at a dramatically increased pace with new and effective, evidence-based COVID-19 interventions rapidly making their way into the clinic. However, the swift generation of high-quality evidence and the efficient processing of regulatory authorisation have given rise to more specific and complex versions of well-known research ethics issues. In this paper, we identify three such issues by focusing on the authorisation of Molnupiravir, a novel antiviral medicine aimed (...) at reducing the ability of SARS-CoV-2 to multiply in the body, for clinical use by the National Health Service in England and the concomitant testing of Molnupiravir through the large-scale PANORAMIC randomised control trial. By analysing the ways in which the authorisation and clinical use of Molnupiravir complicate standard approaches to clinical equipoise, standard of care, and participant consent in the PANORAMIC randomised control trial we will explain some of ethical implications for clinical trials that aim to study the efficacy and safety of new COVID-19 and other therapeutics when conditional authorisation has already been granted and when such treatments have already been made available to patients by national health providers. (shrink)

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what are (...) experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)