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Trust in Scientists and Doctors: The Roles of Faith, Political Orientation, Education and Gender
Get PDFThis article examines trust in science in England ocusing on variation across demographic and ideological groups. Using survey data from 11,173 respondents, we compare trust in two domains, medical doctors and scientists, to explore whether predictors operate similarly across these professional groups. We find higher education is associated with greater trust, while right-wing political orientation predicts lower trust. Religious affiliation also matters, with some faith groups reporting lower
trust relative to the non-religious baseline. Gender differences emerge as well, particularly in trust in medical doctors. Respondents selecting “Prefer not to say” on the religion item report significantly lower trust in both doctors and scientists, consistent with a broader privacy-motivated
disclosure style. Our results highlight the importance of considering not just overall levels of trust in science, but variation across education, ideology, religion and gender, and they suggest that trust in doctors and trust in scientists, while related, are not interchangeable
Reflections on Institutional Corruption in Mental Health Policy Implementation: Global Insights and the Eastern European Experience
Get PDFExisting evidence shows that mental health policies and services are especially vulnerable to ineffective and corrupt practices. Systemic obstacles, such as the overuse of the biomedical model, power asymmetries, and selective evidence, undermine both the realization of the right to health and the rights-based implementation of policies in practice. This paper draws on the personal experience of the authors alongside global insights to examine the relationship between institutional corruption and the right to mental health, with a focus on Central and Eastern Europe as a bellwether. Following the societal transitions of the 1990s and beyond, prolonged psychosocial stress contributed to widespread self-destructive behavior and high mortality rates, particularly among rural, middle-aged men. In response, foreign consultants frequently advised governments to prioritize diagnosing clinical depression and prescribing new-generation psychiatric medications as the principal strategy. We argue that this narrow biomedical focus, reinforced by biased evidence, represents a form of institutional corruption: it distorts problem framing, entrenches biomedical dominance, sidelines community and social responses, and ultimately compromises the right to health. Recognizing and addressing these dynamics is essential to align mental health policy with rights-based, context-responsive care
Deep reinforcement learning for trading strategy development on high-frequency currency data using directional changes sampling
Get PDFHigh-frequency trading in the foreign exchange market presents unique challenges, requiring sophisticated techniques to address its complexities. This thesis investigates the application of deep reinforcement learning to algorithmically trade high-frequency currency data. Traditional trading algorithms typically use fixed interval sampling in both manual and automated trading strategies. While effective for less noisy price movements and longer trade durations, this approach can miss significant price shifts in high-frequency scenarios which limits profitability. To overcome these limitations, this thesis employs the directional change (DC) sampling paradigm, which captures significant price movements more effectively. By combining DC sampled data and deep reinforcement learning to train trading agents, the research explores whether this approach outperforms traditional fixed interval methods when trading at high frequencies. The initial investigation develops the Filtered Deep Reinforcement Learning (FDRL) trading framework, using deep reinforcement learning to create semi-autonomous trading agents. Results show that FDRL is effective at fixed transaction costs but requires rule-based interventions to manage trades. To enhance autonomy, the Positionally Aware Deep Reinforcement Learning (PADRL) framework is introduced, incorporating real-time positional awareness to eliminate the need for rule-based filters, further improving performance. The final contribution of the Spread Aware Deep Reinforcement Learning (SADRL) framework, refines the PADRL approach by using the bid-ask spread as opposed to fixed transaction costs, making the strategy more realistic and applicable to real trading environments. Each algorithm iteration demonstrates improved performance over traditional benchmarks like buy-and-hold and technical analysis. Financial metrics including Total Return, Maximum Drawdown and Calmar Ratio validate the superior performance of these deep reinforcement learning-based strategies, demonstrating their potential for advancing high-frequency trading in the foreign exchange market
Repairing “historical” wrongs: The Church of Sweden’s approach to redressing colonial abuses against the Sami
No full textThis chapter explores the process initiated by the Church of Sweden to repair colonial abuses against Sweden’s indigenous people – the Sami. The Church, formerly the State church and a State institution, played an important role in the colonisation and oppression of the Sami, which led to loss of their traditional lands, language, religion, and culture - the effects of which are still felt today. Since the 1990’s, the Church has worked to address its role in these abuses and to embark on a path towards reparation and reconciliation. This chapter first discusses Sweden’s colonial context and the Church’s involvement in abuses against the Sami. It then examines the legal obligation to provide adequate and effective reparation, before investigating the reparation process initiated by the Church. This includes a critical analysis of the lack of participation by the Sami in the process and of the limits of the Church’s approach to reparation. The chapter argues that while there have been some good faith efforts on the part of the Church, more is needed for the process and substance of reparation to be in line with international human rights law standards
To share or not to share: Public attitudes towards disclosing personal and identifiable medical data and information
Get PDFBackground/Aims
Public perceptions of the acceptability of healthcare organisations, such as the NHS, sharing their data or information with other relevant entities may depend
on various factors. This study aimed to fill gaps in the literature relating to this topic by investigating public perceptions of health record sharing with different entities, and how the anonymity (or lack thereof) of records and the terminology used may affect these attitudes.
Methods
A survey was distributed to 2335 members of the public in England, sampled through YouGov’s online panel. Respondents were randomly sorted into four groups
and given a scenario about health record sharing. The scenarios differed between groups in terms of whether records were personal (non-anonymous) or anonymous, and whether the term ‘data’ or ‘information’ was used. Respondents were asked to rate the acceptability of sharing with different entities, including health and social care providers, insurance companies and local government. Differences between responses were
analysed, with significance set at P<0.01.
Results
The majority (84%) of respondents indicated that they found it either acceptable or very acceptable for the NHS to share personal data and information with hospitals and
GPs. Higher levels of acceptability were observed when the term ‘information’ rather than ‘data’ was used. However, over half of respondents found it either unacceptable or very unacceptable for the NHS to share such information with pharmaceutical companies for
research purposes or with councils, whether these data were personal or anonymised.
Conclusions
This study suggests that people are more willing to share personal data when they perceive there to be potential personal benefits. It also contradicts the commonly held assumption that people are more comfortable sharing records that have been anonymised. These findings could inform future public health initiatives
National culture of secrecy and stock price synchronicity: cross-country evidence
No full textThis study investigates the relationship between the culture of secrecy and stock price comovement using a large sample of firms in 49 countries over the period 1990 to 2019. We find that stock prices in secretive societies comove more than stock prices in less secretive societies. This higher comovement occurs primarily because idiosyncratic volatility is lower. We attribute this finding to cultural biases in secretive societies which deter investors’ information-seeking behavior. To support these conjectures, we provide evidence of stronger mean reversals (less informed trading) in these societies. Our results persist when we account for cross-country differences in firms’ liquidity and information asymmetry, and when we control for cash flow uncertainty. Finally, the enforcement of insider trading laws in secretive countries is associated with less privately informed trading and lower idiosyncratic volatility
Pay Transparency and Gender Equality
Get PDFSince 2018, UK firms with at least 250 employees have been mandated to publicly disclose gender equality indicators. Exploiting variations in this mandate across firm size and time, we show that pay transparency closes 19 percent of the gender pay gap by reducing men’s pay
growth. By combining different sources of data, we also provide suggestive evidence that the public availability of the equality indicators enhances public scrutiny. In turn, employers more exposed to public scrutiny seem to reduce their gender pay gap the most
Identifying The Psyching-Up Strategies Used in Strength Sports: A Concept Mapping Approach
No full textIt has frequently been reported that strength athletes use psyching-up strategies to enhance performance. Despite numerous investigations into the efficacy of these psyching-up strategies, there has yet to be a thorough exploration of the methods used by athletes to do so. Thus, it is important to explore the full breadth of strategies utilized by athletes. The present study aimed to identify the psyching-up strategies used by strength sport athletes and assess the perceived effectiveness on performance. Using a concept mapping approach, 246 strength sport athletes and coaches participated in an initial statement (technique) generation phase, and 112 sorted the techniques into clusters and rated the effectiveness of each technique at enhancing maximal strength performance. In the generation stage, 64 individual psyching-up techniques were identified. Similarity matrix generation, multidimensional scaling, and hierarchical cluster analysis were used to produce visual cluster maps, which identified eight separate clusters of psyching-up strategies: “pre-performance routines”; “positive thoughts, feelings, images, and behaviors”; “goals and performance accomplishments”; “self-deprecation”; “negative thoughts, feelings, images, and behaviors”; “stimulation”; “physical and physiological techniques”; and “aggressive acts”. Participants ranked “pre-performance routines” as being the most effective psyching-up strategy, with males reporting significantly higher ratings for “self-deprecation”; “negative thoughts, feelings, images, and behaviors”; “stimulation”; and “aggressive acts”. The present findings demonstrated a greater breadth of psyching-up techniques than those currently examined within the literature. Accordingly, we suggest a revised definition of psyching-up strategies in the context of strength sports: “strategies intending to alter activation or to enhance mental preparedness, immediately prior or during skill execution”
Automated Detection of Emotion in Central Bank Communication: A Warning
Get PDFCentral banks have increased their official communications. Previous literature measures
complexity, clarity, tone and sentiment. Less explored is the use of fact versus emotion in
central bank communication. We test a new method for classifying factual versus emotional
language, applying a pretrained transfer learning model, fine-tuned with manually coded, task-
specific, and domain-specific datasets. We find that the large language models outperforms
traditional models on some occasions, however, the results depend on a number of choices. We
therefore caution researchers from depending solely on such models even for tasks that appear
similar. Our findings suggest that central bank communications are not only technically difficult
but also subjectively difficult to understan