Psychedelically-enhanced psychotherapy (PAP) looks set to become a common remedy for a range of serious mental health problems. The market for providing PAP, including a secondary market for the training, credentialising and monitoring of therapists, is expanding rapidly. Concerns have been raised recently by actors in that secondary market about the potential for abuse in PAP, which have been framed in terms of a failure to respect patient autonomy. Such concerns cannot be adequately addressed without a fundamental reconsideration of the role of autonomy in psychotherapy. Discussing what autonomy means in psychotherapy and thence especially in PAP is the aim of this chapter, which starts from practitioner-focused guidance, before reflecting on the history of autonomy in geopolitics and ethics and finally returning to consider its place in psychotherapy generally and PAP specifically. The conclusion reached is that while protecting autonomy is the primary concern of medical ethics today, autonomy is not equal to the phenomenology of the psychedelic experience, which is better characterised in terms of 'autoheteronomy'. The chapter's contribution to the emerging 'psychedelic humanities' is to show that PAP brings to crisis longstanding cultural compromises and uncertainties around the way in which psychotherapy has been thought to foster patient autonomy.
The killing of Micah Johnson by Dallas police using a teleguided exploding robot on 8 July 2016 is the first known example of the use of a killer drone by US law enforcement in the domestic arena. This repatriation of the drone, under conditions of racialized urban unrest designated as exceptional, was predicted by Didier Bigo and follows a familiar pattern whereby coercive security technologies are tested abroad before finding their way 'home' to arm police forces that are becoming increasingly paramilitary in style and conduct. I use the Dallas incident to probe the cogency and limits of 'drone theory' and to consider its application in domestic policing contexts. I work through three broadly delineated areas of scepticism about drone theory as it intersects with policing and, in so doing, develop my own account of the weaponized policing drone as a defining techno-cultural element within the emergent form of neoliberal political rationality I call 'governance'.
This article suggests that Eribon's autobiography is most engaging (as literature) and valuable (as social document) in those moments when the author loses his interpretative grip on the meaning of his own experience. Although a concerted attempt is made by Eribon to account for his problematic relationship to his working-class family background, in particular his father, in purely sociological terms, a restive textual indeterminacy at key junctures unwittingly exposes the limitations of this approach. By allowing us to glimpse the limits of its author's sociological rationalism, the autobiography calls into question Eribon's strategic rejection of psychoanalytic forms of understanding and a number of his other longstanding theoretical and political commitments. This is just as it should be: its restive moments and the critical consequences which follow from them make Eribon's autobiography much more than a mere exercise in self-validation.
"What does 'autonomy' mean today? Is the Enlightenment understanding of autonomy still relevant for contemporary challenges? How have the limits and possibilities of autonomy been transformed by recent developments in artificial intelligence and big data, political pressures, intersecting oppressions and the climate emergency? The challenges to autonomy today reach across society with unprecedented complexity, and in this book leading scholars from philosophy, economics, linguistics, literature and politics examine the role of autonomy in key areas of contemporary life, forcefully defending a range of different views about the nature and extent of resistance to autonomy today. These essays are essential reading for anyone who wants to understand the predicament and prospects of one of modernity's foundational concepts and one of our most widely cherished values"--
Draws on Jacques Rancière's thesis in Hatred of Democracy to help explain the aversion to sex that is evident in numerous forms in the culture around us.
Introduction & BackgroundSocial media data is increasingly recognised as an important source of behavioural data. It can provide insights into patterns of life and how individuals and groups are feeling. However, many studies into social media's relationship to mental health and well-being have suffered from poorly developed ground-truth data, which relies on assumed ground-truth labels and data from single timepoints. This means that the accuracy of models at future timepoints cannot be assessed.
Collecting Twitter data from cohorts provides a solution to this issue, given the many years of high quality data that can be used as ground truth. Cohorts can also benefit from the higher-resolution data provided by social media that can supplement their traditional data collection methods.
Objectives & ApproachWe used Twitter data that has been collected with consent from two generations of the Avon Longitudinal Study of Parents and Children (ALSPAC) (N=656). The data is linked to two surveys completed in April-May 2020 and May-July 2020 for validated outcome measures of anxiety, depression, and general well-being.
Using the LIWC and VADER sentiment algorithms, the sentiment categories most highly associated with each outcome were used to develop a multiple regression model for each of anxiety, depression and general well-being using the first survey timepoint. Error from these models in predicting the second timepoint allowed us to assess how well different outcomes are predicted by demographic group.
Relevance to Digital FootprintsDigital footprint data can complement traditional data sources to provide a more nuanced view of health inequalities. These data are typically less timely to collect than traditional data collection methods (census, survey) allowing a more reactive response to emergent issues such as the cost-of-living crisis.
ResultsThis study illustrates how the collection of digital footprint data can be integrated into existing long-term studies which can be used to provide multiple points of ground-truth data.
Conclusions & ImplicationsThis study has shown that the collection and integration of Twitter data into cohort studies is feasible, and that cohort data provides multiple ground-truth options. This time series data is important for assessing the potential feasibility of mental health inference from online behavioural data, which this study shows may vary across personal characteristics.
In future research we plan to link subsequent surveys from ALSPAC to provide more ground truth time points and explore the temporal stability of predictions, and impacts of model drift on performance.
Introduction & BackgroundHealth research using digital footprint data often involves the collection and use of large datasets that contain deeply personal information to make inferences about the course and onset of illness. In this context, innovating responsibly is essential for the field to develop safe, trustworthy and, ultimately, ethical research.
The inherent interdisciplinarity of digital footprints research can be a challenge to this aim, with different fields having different ethical norms and standards. As well as this, there has been a strong focus to date on traditional ethical issues such as privacy, which do not necessarily account for the breadth of issues that arise in data science and internet-based work.
Objectives & ApproachData Hazards is an open-source project that aims to provide a controlled vocabulary of ethical risks (Data Hazards) that can arise from data science research and its implementation. This vocabulary is presented as a set of 11 Hazard labels (v1.0) each with a visual icon and a set of safety precautions.
Over three events in 2021-2022 we invited feedback from researchers who volunteered to take part in a Data Hazards workshop (N=15). They varied from PhD students to professors and worked across a range of disciplines, and were asked to discuss the case of mental health prediction from Twitter.
Relevance to Digital FootprintsSince digital footprint technologies have great potential to pave the way for earlier and more personal medical treatment, it is important for researchers to be able to innovate whilst considering and communicating risk. We can then collaborate to establish effective safety precautions that allow us to maintain research momentum, without compromising safety or trust.
ResultsBased on discussion at the workshops and surveys completed by participants, four main Data Hazards were raised for consideration by the digital footprint research community. These were: 'Lack of Community Involvement' relating to the need to further involve those with lived experience in the development of new technologies; 'Reinforces Existing Bias' due to the potential for automated labelling of ground-truth data to bias training datasets; 'Privacy' given the potential disclosure of sensitive information without consent; and 'Danger of Misuse' due to strong potential for malicious use of such technologies.
Other considerations included the potential psychological risk to those labelling suicide and self-harm content with limited support.
Conclusions & ImplicationsThe Data Hazards identified provide a means of communicating and clarifying ethical concerns so that they can be more easily addressed in this complex and multidisciplinary field. Further collaboration by the research community to develop and agree appropriate safety precautions would help to build trust in these new technologies before they are deployed in practice.
Introduction & BackgroundSocial media use has been proposed as a cause of worsening mental health and wellbeing over the last decade, but its role in mitigating some of the effects of social distancing during the pandemic showed that it also has the potential to improve these outcomes. Whilst existing research disagrees on the degree to which social media use harms or helps, there is growing consensus around the need to move from global measures of social media use to specific measures of types of social media use. These new measures can enable an exploration of proposed mechanisms and causal pathways linking social media use and mental health and wellbeing. A commonly proposed mechanism is nighttime social media use reducing sleep quality, and consequently harming mental health and wellbeing.
Objectives & ApproachWe aimed to investigate the relationships between the time Twitter users post content and their mental health, wellbeing and sleep quality using direct measurements of Twitter use linked to standardised mental health measures in a well-characterized cohort.
This study uses approximately 1.5 million Tweets harvested between January 2008 and March 2023 from 622 participants in the Avon Longitudinal Study of Parents and Children (ALSPAC). These Tweets have been linked to questionnaire data collected on six occasions spanning April 2019 to May 2021. These questionnaires included standard measures of depressive symptoms, anxiety symptoms, mental wellbeing and difficulty sleeping.We have taken two approaches to explore these relationships, using circular statistical methods novel to social media data analysis to account for day/night cycles. The first approach used mixed effect models to investigate the association between the time a Tweet was posted and the mental health, mental wellbeing and sleep quality of the poster. The second approach explored the relationships between the mean hour participants post Tweets in a given time period, and their mental health, mental wellbeing and sleep quality.
Relevance to Digital FootprintsThis research is highly relevant to Digital Footprints, due to its use of data directly extracted from a social media site. The methodologies employed in analysing this alongside more traditional epidemiological survey data provides an example of how digital footprint data can complemented by high quality ground truths.
ResultsThere was evidence that the timing of Twitter activity was predictive of the mental wellbeing and sleep quality of participants, even after adjustment for demographic, educational and socio-economic covariates. However, the hour a Tweet was posted at explained very little of the variation in the mental wellbeing or sleep quality of the participant who posted it (0.1% and less than 0.1% respectively). There was weak to no evidence that the timing of Twitter activity was predictive of the depressive and anxiety symptoms of participants.
Conclusions & ImplicationsWhilst this study found evidence that the hour participants post on Twitter is predictive of their mental wellbeing and sleep quality, the amount of variation explained by these models suggests that this is not a clinically relevant risk factor. This study supports arguments in the literature that the use of social media has a very small and insignificant effect on mental health, wellbeing and sleep quality.
