What is the relationship between morality and politics? If morality is a collection of cooperative rules, and politics is conflict over which cooperative projects to pursue, then we should expect the two to be related. People who expect to benefit from a particular type of cooperation will be likely to endorse the corresponding moral values and political policies. Here we examine the relationship between moral values and political liberalism-conservatism, with data from the Morality-As-Cooperation Questionnaire and the Social and Economics Conservatism Scale in samples of participants from the USA (N = 518), Denmark (N = 552), the Netherlands (N = 353), and an international online population (N = 1,337). We found that political conservatism was consistently related with deference values. We also found some support for the hypotheses that political orientation has different associations with family values versus group values and has different associations with fairness values versus reciprocity values. However, for most of our hypotheses, the results showed no support or did not reach statistical significance, largely due to poor model fit or measurement error associated with the political scales. We conclude that improved measurement of political preferences is needed to illuminate the relationship between morality and politics.

ObjectivesThe UK government’s approach to the pandemic relies on a test, trace and isolate strategy, mainly implemented via the digital NHS Test & Trace Service. Feedback on user experience is central to the successful development of public-facing services. As the situation dynamically changes and data accumulate, interpretation of feedback by humans becomes time-consuming and unreliable. The specific objectives were to 1) evaluate a human-in-the-loop machine learning technique based on structural topic modelling in terms of its serviceability in the analysis of vast volumes of free-text data, 2) generate actionable themes that can be used to increase user satisfaction of the Service.MethodsWe evaluated an unsupervised Topic Modelling approach, testing models with 5-40 topics and differing covariates. Two human coders conducted thematic analysis to interpret the topics. We identified a Structural Topic Model with 25 topics and metadata as covariates as the most appropriate for acquiring insights.ResultsResults from analysis of feedback by 37,914 users from May 2020 to March 2021 highlighted issues with the Service falling within three major themes: multiple contacts and incompatible contact method and incompatible contact method, confusion around isolation dates and tracing delays, complex and rigid system.ConclusionsStructural Topic Modelling coupled with thematic analysis was found to be an effective technique to rapidly acquire user insights. Topic modelling can be a quick and cost-effective method to provide high quality, actionable insights from free-text feedback to optimize public health services.

Reddit’s subforum ChangeMyView is a subreddit where users post about a specific view they hold and chal- lenge other users to present arguments to change their view. Here, we investigated what distinguishes the most convincing participants on Reddit change my view (henceforth, top persuaders), as measured by the all- time deltaboard, from equally active users who are nonetheless less likely to persuade peoples on the fo- rum. We investigated the most common features of top persuaders and compared them to their similarly active Reddit counterparts. We found that top persuaders were more likely to provide external evidence for their claims and use morality-based reasoning, made longer com- ments and engaged in more back-and-forth argumenta- tion, were less likely to use informal language in their arguments, and were more likely to show some over- lapping semantic content with an original poster’s com- ment. Other content was comparatively less effective: including the use of causal language, and asking ques- tions in an argument, among others. These findings replicate and expand on previously established effects and may highlight ways to develop more effective edu- cational interventions.

The growing demand for novel therapies has raised new ethical dilemmas for society. Vigorous debates have been initiated, especially after the recent Right to Try Act in the US, which aims to facilitate access to new agents, even at an early stage of the investigation process. This article explores the concept of patient’s autonomy in accessing investigational treatments, and discusses the implications of autonomy for patients, researchers and regulatory authorities. We propose that, in cases of adequate understanding of the anticipated risks and benefits, society should accept patients’ autonomy in choosing to try new therapies, even in the absence of firm data. However, basic ethical principles should not be compromised, as the medical community and society as a whole retain the right to properly evaluate the accumulated experience from such cases. These thoughts may contribute to the ongoing discussions on ethical policies in clinical research.

BackgroundMachine-assisted topic analysis (MATA) uses artificial intelligence methods to assist qualitative researchers to analyse large amounts of textual data. This could allow qualitative researchers to inform and update public health interventions ‘in real-time’, to ensure they remain acceptable and effective during rapidly changing contexts (such as a pandemic).ObjectiveWe aimed to understand the potential for such approaches to support intervention implementation, by directly comparing MATA and ‘human-only’ thematic analysis techniques when applied to the same dataset (1472 free-text responses from users of the COVID-19 infection control intervention ‘Germ Defence’).MethodsIn MATA, the analysis process included an unsupervised topic modelling approach to identify latent topics in the text. The human research team then described the topics and identified broad themes. In human-only codebook analysis, an initial codebook was developed by an experienced qualitative researcher and applied to the dataset by a well-trained research team, who met regularly to critique and refine the codes. To understand similarities and difference, formal triangulation using a ‘convergence coding matrix’ compared the findings from both methods, categorising them as ‘agreement’, ‘complementary’, ‘dissonant’, or ‘silent’.ResultsHuman analysis took much longer (147.5 hours) than MATA (40 hours). Both human-only and MATA identified key themes about what users found helpful and unhelpful (e.g. Helpful: Boosting confidence in how to perform the behaviours. Unhelpful: Lack of personally relevant content). Formal triangulation of the codes created showed high similarity between the findings. All codes developed from the MATA were classified as in agreement or complementary to the human themes. Where the findings were classified as complementary, this was typically due to slightly differing interpretations or nuance present in the human-only analysis.ConclusionsOverall, the quality of MATA was as high as the human-only thematic analysis, with substantial time savings. For simple analyses that do not require an in-depth or subtle understanding of the data, MATA is a useful tool that can support qualitative researchers to interpret and analyse large datasets quickly. These findings have practical implications for intervention development and implementation, such as enabling rapid optimisation during public health emergencies.