Integrating large language models (LLMs) like GPT-4 into medical ethics is a novel concept, and understanding the effectiveness of these models in aiding ethicists with decision-making can have significant implications for the healthcare sector. Thus, the objective of this study was to evaluate the performance of GPT-4 in responding to complex medical ethical vignettes and to gauge its utility and limitations for aiding medical ethicists. Using a mixed-methods, cross-sectional survey approach, a panel of six ethicists assessed LLM-generated responses to eight (...) ethical vignettes.The main outcomes measured were relevance, reasoning, depth, technical and non-technical clarity, as well as acceptability of GPT-4’s responses. The readability of the responses was also assessed. Of the six metrics evaluating the effectiveness of GPT-4’s responses, the overall mean score was 4.1/5. GPT-4 was rated highest in providing technical (4.7/5) and non-technical clarity (4.4/5), whereas the lowest rated metrics were depth (3.8/5) and acceptability (3.8/5). There was poor-to-moderate inter-rater reliability characterised by an intraclass coefficient of 0.54 (95% CI: 0.30 to 0.71). Based on panellist feedback, GPT-4 was able to identify and articulate key ethical issues but struggled to appreciate the nuanced aspects of ethical dilemmas and misapplied certain moral principles.This study reveals limitations in the ability of GPT-4 to appreciate the depth and nuanced acceptability of real-world ethical dilemmas, particularly those that require a thorough understanding of relational complexities and context-specific values. Ongoing evaluation of LLM capabilities within medical ethics remains paramount, and further refinement is needed before it can be used effectively in clinical settings. (shrink)

Self-relevance has been demonstrated to impair instrumental learning. Compared to unfamiliar symbols associated with a friend, analogous stimuli linked with the self are learned more slowly. What is not yet understood, however, is whether this effect extends beyond arbitrary stimuli to material with intrinsically meaningful properties. Take, for example, stimulus valence an established moderator of self-bias. Does the desirability of to-be-learned material influence self-learning? Here, in conjunction with computational modelling (i.e. Reinforcement Learning Drift Diffusion Model analysis), a probabilistic selection task (...) was used to establish if and how stimulus valence (i.e. desirable/undesirable posters) impacts the acquisition of knowledge relating to object-ownership (i.e. owned-by-self vs. owned-by-friend). Several interesting results were observed. First, undesirable posters were learned more rapidly for self compared to friend, an effect that was reversed for desirable posters. Second, learning rates were accompanied by associated differences in reward sensitivity toward desirable and undesirable choice selections as a function of ownership. Third, decisional caution was greater for self-relevant (vs. friend relevant) responses. Collectively, these findings inform understanding of self-function and how valence and stimulus relevance mutually influence probabilistic learning. (shrink)

High quality monitoring of mental health and well-being over an extended period is essential to understand how communities respond to the COVID-19 pandemic and how to best tailor interventions. Multiple community threats may also have cumulative impact on mental health, so examination across several contexts is important. The objective of this study is to report on changes in mental health and well-being in response to the Australian bushfires and COVID-19 pandemic. This study utilized an Experience-Sampling-Method, using the smartphone-based mood monitoring (...) application, MoodPrism. Participants were prompted once a day to complete a brief survey inquiring about symptoms of depression and anxiety, and several well-being indices, including arousal, emotional valence, self-esteem, motivation, social connectedness, meaning and purpose, and control. Participants were N = 755 Australians who downloaded and used MoodPrism, between 2018 and 2020. Results showed that anxiety symptoms significantly increased during the COVID-19 pandemic, but not during the bushfires. This may be explained by concurrent feelings of social connectedness maintained during the bushfires but not during the pandemic. In contrast, depressive symptoms increased significantly during the bushfires, which maintained during the pandemic. Most indices of well-being decreased significantly during the bushfires, and further again during the pandemic. Study findings highlight the unique responses to the bushfire and COVID-19 crises, revealing specific areas of resilience and vulnerability. Such information can help inform the development of public health interventions or individual clinical treatment, to improve treatment approaches and preparedness for potential future community disasters. (shrink)

The traditional view of cognition as detached from emotions is recently being questioned. This study aimed to investigate the influence of emotional valence on the accuracy and bias in the representation of numbers on the mental number line (MNL). The study included 164 participants who were randomly assigned into two groups with induced positive and negative emotional valence using matched arousal film clips. Participants performed a computerised number-to-position (CNP) task to estimate the position of numbers on a horizontal line. The (...) results showed that participants in the positive valence group exhibited a rightward bias, while those in the negative valence group showed an opposite pattern. The analysis of mean absolute error revealed that the negative valence group had higher error rates compared to the positive valence group. Furthermore, the MNL estimation pattern analysis indicated that a two-cycle cyclic power model (CPM) best explained the data for both groups. These findings suggest that emotional valence influences the spatial representation of numbers on the MNL and affects accuracy in numerical estimations. Our findings are finally discussed in terms of body-specificity and the Brain’s Asymmetric Frequency Tuning (BAFT) theories. The study provides new insights into the interplay between emotions and numerical cognition. (shrink)

Publication date: 11 June 2018 Source: Author: Fatemeh Nemati Sogolitappeh, Amjad Hedayat, Mehrdad Rezaee Arjmand, Mohamad Khaledian The purpose of the present study is to investigate the relationship between spiritual intelligence and emotional intelligence with resilience in undergraduate students. The population of the study included all students of Payam Noor University in Ghorveh during 2016-2017 academic years. According to the principles of statistical methodology for correlational studies, 100 students from different disciplines were selected as sample of the study using (...) simple random sampling method. In this research, King's Spiritual Intelligence Questionnaire, Bar-Ann 90-questions Emotional Intelligence Questionnaire, and Conner and Davison resilience questionnaire were used. Descriptive statistics and inferential statistics were used to analyze the data. The results of this study showed that there is a significant positive correlation between spiritual intelligence and emotional intelligence and resilience. Regression coefficients showed that spiritual intelligence and emotional intelligence predict 53.9% of the variation in resiliency. (shrink)