Effects of perspective switching and utilitarian thinking on moral judgments in a sacrificial dilemma among healthcare and non-healthcare students.

During the COVID-19 pandemic, healthcare professionals have often faced moral challenges, which required them to choose between endorsing self- or other-sacrifice for the greater good. Drawing on the altruistic rationalization hypothesis and trait-activation theory, this study investigates (a) whether healthcare students' endorsement of utilitarian solutions to sacrificial moral dilemmas varies when they are confronted with the minority group, majority group, or third-person perspective on the given dilemma and (b) whether individual differences in utilitarian thinking, as measured by the Oxford Utilitarianism Scale (both instrumental harm and impartial beneficence), predict endorsement of utilitarian solutions to moral dilemmas. The study population was divided into a group of healthcare students and a group of non-healthcare students. It was found that the members of both groups expressed a stronger pro-utilitarian position when making moral dilemma judgments from a majority perspective than from the two other perspectives. However, a difference was observed with healthcare students being more reluctant to endorse the utilitarian action than their non-healthcare counterparts in the self-in-majority context. The instrumental harm component was a significant predictor of utilitarian judgments in the healthcare group, but impartial beneficence significantly predicted utilitarian judgments in the non-healthcare group in the self-in-majority context. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-023-04380-z.

The effect of deliberative process on the self-sacrificial decisions of utilitarian healthcare students.

BACKGROUND: The COVID-19 pandemic has highlighted prosocial behavior as a professional healthcare core competency. Although medical students are expected to work in the best interests of their patients, in the pandemic context, there is a greater need for ethical attention to be paid to the way medical students deal with moral dilemmas that may conflict with their obligations. METHODS: This study was conducted in the spring semester of 2019 on 271 students majoring in health professions: medicine, dentistry, and veterinary medicine. All participants provided informed consent and completed measures that assessed utilitarian moral views, cognitive reflections, cognitive reappraisal, and moral judgment. RESULTS: The healthcare-affiliated students who scored higher on the instrumental harm subscale in the measurement of utilitarian moral views were more likely to endorse not only other-sacrificial actions but also self-sacrificial ones for the greater good in moral dilemma scenarios. In particular, those engaged in deliberative processes tended to make more self-sacrificial judgments. The mediation analysis also revealed that the effect of deliberative processes on self-sacrificial judgments was mediated by cognitive reappraisal. CONCLUSIONS: These findings suggested that cognitive reappraisal through deliberative processes is involved when the students with utilitarian inclination make prosocial decisions, that it is necessary to consider both moral views and emotional regulation when admitting candidates, and that moral education programs are needed in the healthcare field.

PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks.

Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models. Although various explanation methods for GNNs have been developed, most studies have focused on instance-level explanations, which produce explanations tailored to a given graph instance. In our study, we propose Prototype-bAsed GNN-Explainer ([Formula: see text]), a novel model-level GNN explanation method that explains what the underlying GNN model has learned for graph classification by discovering human-interpretable prototype graphs. Our method produces explanations for a given class, thus being capable of offering more concise and comprehensive explanations than those of instance-level explanations. First, [Formula: see text] selects embeddings of class-discriminative input graphs on the graph-level embedding space after clustering them. Then, [Formula: see text] discovers a common subgraph pattern by iteratively searching for high matching node tuples using node-level embeddings via a prototype scoring function, thereby yielding a prototype graph as our explanation. Using six graph classification datasets, we demonstrate that [Formula: see text] qualitatively and quantitatively outperforms the state-of-the-art model-level explanation method. We also carry out systematic experimental studies by demonstrating the relationship between [Formula: see text] and instance-level explanation methods, the robustness of [Formula: see text] to input data scarce environments, and the computational efficiency of the proposed prototype scoring function in [Formula: see text].

Unraveling motives: identifying the impact of university attendance motives on learning behaviors among dental students.

PURPOSE: Students report various motives for attending university (MAU) grouped under five categories, namely, personal-intellectual development (PER), humanitarian (HUM), careerist-materialist (CAR), expectation-driven (EXP), and uncertain motives. Although the literature demonstrates that these motives exert an influence on learning and achievement, relatively less attention is given to this issue in the context of dental students. This study aimed to examine the relationship among the mindsets, MAU, academic engagement (AE), and DAL of dental students and to test the mediating effect of AE on the relationship between MAU and deep approach to learning (DAL). METHODS: The study recruited 226 dental students at various levels of the curriculum, who responded to four questionnaires for measuring MAU, DAL, mindsets, and AE. The study employed structural equation modeling to analyze the mediation effects of AE on the relationship between MAU and DAL and to determine the influence of mindsets on MAU. RESULTS: This model reveals the significant relationships of a growth mindset with CAR, PER, and HUM. Moreover, the study finds that a fixed mindset was associated with CAR, EXP, and uncertain motives. Furthermore, AE only fully mediated the significant positive relationship between PER and DAL, whereas CAR negatively predicted DAL without a mediator. CONCLUSIONS: These findings suggest that administering the inventories in a dental school setting can facilitate a more comprehensive understanding of students' mindsets toward learning and effective processes related to learning. This understanding can inform instructors' pedagogical practices, enabling them to provide more effective guidance to students navigating the complexities of academic coursework.

Effects of virtual body-representation on motor skill learning.

Motor learning is often hindered or facilitated by visual information from one's body and its movement. However, it is unclear whether visual representation of the body itself facilitates motor learning. Thus, we tested the effects of virtual body-representation on motor learning through a virtual reality rotary pursuit task. In the task, visual feedback on participants' movements was identical, but virtual body-representation differed by dividing the experimental conditions into three conditions: non-avatar, non-hand avatar, and hand-shaped avatar. We measured the differences in the rate of motor learning, body-ownership, and sense of agency in the three conditions. Although there were no differences in body-ownership and sense of agency between the conditions, the hand-shaped avatar condition was significantly superior to the other conditions in the rate of learning. These findings suggest that visually recognizing one's body shape facilitates motor learning.

Explainable gait recognition with prototyping encoder-decoder.

Human gait is a unique behavioral characteristic that can be used to recognize individuals. Collecting gait information widely by the means of wearable devices and recognizing people by the data has become a topic of research. While most prior studies collected gait information using inertial measurement units, we gather the data from 40 people using insoles, including pressure sensors, and precisely identify the gait phases from the long time series using the pressure data. In terms of recognizing people, there have been a few recent studies on neural network-based approaches for solving the open set gait recognition problem using wearable devices. Typically, these approaches determine decision boundaries in the latent space with a limited number of samples. Motivated by the fact that such methods are sensitive to the values of hyper-parameters, as our first contribution, we propose a new network model that is less sensitive to changes in the values using a new prototyping encoder-decoder network architecture. As our second contribution, to overcome the inherent limitations due to the lack of transparency and interpretability of neural networks, we propose a new module that enables us to analyze which part of the input is relevant to the overall recognition performance using explainable tools such as sensitivity analysis (SA) and layer-wise relevance propagation (LRP).

Did Clinical Reasoning and Knowledge Questions During Team-Based Learning Enhance Dental Students' Performance in Esthetic Dentistry?

The aim of this study was to assess whether clinical reasoning and factual knowledge questions used in team-based learning (TBL) enhanced dental students' performance in esthetic dentistry. Ninety-seven third-year dental students enrolled in esthetic dentistry in a dental school in Korea in 2018 were assigned to 16 teams consisting of five or six students each. A four-step TBL sequence (pre-study, readiness assurance test, appeal/feedback, and final test) was designed to examine how clinical reasoning and knowledge questions affected academically high- and low-achieving students. The analysis was conducted with 87 students' data because ten students failed to answer some questions. The results showed that team performance in TBL was consistently better than individual performance. The TBL sessions enhanced students' critical thinking skills, though it did not affect their knowledge acquisition. The clinical reasoning questions especially benefited the academically low-achieving students. Overall, TBL was an effective method for teaching these dental students using small-group learning in esthetic dentistry. Team-based cooperative learning facilitated a deeper understanding of esthetic dentistry because students were motivated to think critically and solve problems rather than simply memorize factual knowledge.