Search Results (2,434)

Nostalgic scenes can trigger nostalgia to a considerable extent and can be effectively used as a nostalgic trigger that contributes to the psychological comfort of the elderly and immigrant populations, but a design system has not been adequately studied. Therefore, the design principles and digital twin (DT) design system of nostalgic scenes is proposed in this study. It focuses on the construction of a nostalgic scene DT model based on the system of system (SoS) theory. Nostalgic scenes related to farm work are selected and photos of this DT model from a particular perspective are generated for presentation. Co-occurrence analysis is used to verify the correlation between elements within the scene. We invited two groups of residents in Xi’an, the regional group and the non-regional group, a total of 68 people, as participants to rate three photos with different degrees of design on the Likert scale. The results of data analysis show that systematic and well-composed nostalgic scene images, which incorporate relevant elements, are more likely to evoke participants’ nostalgic emotions than ones without those elements mentioned above. Likewise, a series of nostalgic scene images spanning various periods can stimulate participants’ nostalgic emotions more effectively than a single image. Furthermore, region-specific nostalgic scene images that resonate with participants sharing similar lifestyles can trigger their nostalgic feelings more effectively. The digital twin model of the nostalgia scene contains multi-source data, which can be dynamically visualised to represent regional nostalgic experiences. The design system can be used to design nostalgic scenes to improve emotional health, social bonding, tourism, and sustainable urban and rural development. Full article

Introduction/Background: Burnout is a three-dimensional syndrome characterized by exhaustion that appears when the professional is constantly exposed to a stressful work environment, as well as depersonalization and lower personal accomplishment. Professional quality of life at work can be defined as the satisfaction degree that a person feels when being or going to their workplace. Objective: To evaluate burnout and professional quality of life in healthcare professionals working in oncology and palliative care. Material and methods: A cross-sectional, observational, and descriptive study was carried out in a convenience sample of 337 healthcare professionals from a Portuguese Oncology Hospital. The assessment tools were a sociodemographic questionnaire, the Maslach Burnout Inventory (MBI), and the Professional Quality of Life—version 5 (proQOL-5) scales. Statistical analysis was performed using the IBM SPSS^® Statistics program (significance level of 95% (p ≤ 0.05)). Results: The majority of professionals were female (84%), with a median age of 41 years. Most professionals work in oncological care services (76.8%), with an average of 40 h a week. There were no statistically significant differences in MBI and ProQOL dimensions between the two groups studied (professionals working in oncology vs. palliative care). In the sample and group of professionals working with palliative patients, it was observed that lower levels of compassion satisfaction were related to higher levels of emotional exhaustion (p < 0.001). On the other hand, higher levels of satisfaction correlated with a greater sense of personal accomplishment (p < 0.001); higher levels of secondary traumatic stress were related to a greater tendency towards emotional exhaustion (p < 0.001) and depersonalization (p = 0.031). Discussion and conclusions: Working in oncology and palliative care may predispose one to the onset of burnout related to contact with distress and suffering. With this study, the authors intend to demonstrate that both scales (MBI and ProQOL) are complementary in the analysis of the prevalence of burnout and professional quality of life, particularly in the context of oncology and palliative care. The risk of compassion fatigue and burnout highlights the need to develop coping strategies to minimize this risk and improve the quality of life and bonding of health professionals. Full article

Detecting deceptive behaviour for surveillance and border protection is critical for a country’s security. With the advancement of technology in relation to sensors and artificial intelligence, recognising deceptive behaviour could be performed automatically. Following the success of affective computing in emotion recognition from verbal and nonverbal cues, we aim to apply a similar concept for deception detection. Recognising deceptive behaviour has been attempted; however, only a few studies have analysed this behaviour from gait and body movement. This research involves a multimodal approach for deception detection from gait, where we fuse features extracted from body movement behaviours from a video signal, acoustic features from walking steps from an audio signal, and the dynamics of walking movement using an accelerometer sensor. Using the video recording of walking from the Whodunnit deception dataset, which contains 49 subjects performing scenarios that elicit deceptive behaviour, we conduct multimodal two-category (guilty/not guilty) subject-independent classification. The classification results obtained reached an accuracy of up to 88% through feature fusion, with an average of 60% from both single and multimodal signals. Analysing body movement using single modality showed that the visual signal had the highest performance followed by the accelerometer and acoustic signals. Several fusion techniques were explored, including early, late, and hybrid fusion, where hybrid fusion not only achieved the highest classification results, but also increased the confidence of the results. Moreover, using a systematic framework for selecting the most distinguishing features of guilty gait behaviour, we were able to interpret the performance of our models. From these baseline results, we can conclude that pattern recognition techniques could help in characterising deceptive behaviour, where future work will focus on exploring the tuning and enhancement of the results and techniques. Full article

Shift work nurses suffered great stress and emotion dysregulation during the COVID-19 pandemic. Interpersonal emotion regulation has emerged as a promising therapeutic approach, often facilitated through confiding. It has been suggested that medical staff benefit from confiding, with the act of reflecting on the social support gained from confiding being associated with higher well-being. Consequently, we hypothesized that thinking about the social support derived from confiding about work-related hassles could enhance emotion regulation and well-being in shift work nurses. This study aimed to evaluate the effects of the intervention “thinking about the social support obtained from confiding about work-related hassles” on shift work nurses’ emotion regulation and self-perceived well-being. An online randomized controlled trial was conducted with 66 shift work nurses, including 34 in the experimental group and 32 in the control group, to assess the impact of an 8-week confiding intervention focused on thinking about the social support obtained from confiding. The results indicated that the intervention significantly improved the interpersonal emotion regulation of shift work nurses in the experimental group compared to the control group. In terms of intrapersonal emotion regulation, the intervention appeared to reduce the cognitive reappraisal in the intervention group; however, there was no significant difference in cognitive reappraisal or expressive inhibition between the intervention group and control group. Furthermore, self-rated general health and sleep quality showed significant improvement in the intervention group compared to pre-test levels, but no significant differences were observed between the experimental and control groups. In conclusion, the online confiding intervention effectively enhanced interpersonal emotion regulation among shift work nurses. However, its effects on intrapersonal emotion regulation were not significant. Similarly, while participants in the intervention group reported improved self-rated general health and sleep quality, these improvements did not significantly differ from those in the control group. Full article

Cancer is the leading cause of death in Canada, with diagnoses increasing annually. In Alberta, many cancer cases are detected in emergency departments, often at advanced stages. Despite the significant role of emergency departments in cancer diagnosis, limited research exists on the experiences of healthcare providers in this context. This qualitative study aimed to explore the perspectives of physicians and nurses working in emergency departments in Edmonton and Calgary regarding cancer diagnosis. Semi-structured interviews were conducted with 17 physicians and nurses, recruited through convenience and snowball sampling. Data collection continued until thematic saturation was reached. Interviews were analyzed thematically using an inductive, iterative process. Three main themes emerged: the acute care focus of the emergency department, its unsuitability for cancer diagnosis, and the need for systemic improvements to better support patients with suspected cancer. Participants highlighted challenges related to high patient volumes, the emotional burden of delivering cancer diagnoses, and barriers to effective communication and patient interaction in a fast-paced, high-pressure environment. The findings suggest the need for systemic reforms, including stronger primary care and improved care coordination, to alleviate pressure on emergency departments and enhance both patient outcomes and healthcare provider well-being. Full article

Objectives: To evaluate the influence of job demands and resources on burnout risk among Italian pediatric neuropsychiatrists. Methods: This cross-sectional study was conducted between December 2023 and February 2024 and involved Italian pediatric neuropsychiatrists. The study applied the Job Demands-Resources (JD-R) model to assess the impact of job demands (such as work–family conflict, time pressure, and job uncertainty) and job resources (like organizational support and perceived job meaning) on burnout. Burnout was measured through emotional exhaustion, depersonalization, and personal accomplishment subscales. Demographic data, including gender and career stage, were analyzed for their association with burnout. Results: High job demands were significantly associated with increased emotional exhaustion and depersonalization, while greater job resources correlated with lower burnout levels and higher personal accomplishment. Gender differences emerged, with female neuropsychiatrists reporting significantly higher emotional exhaustion and lower personal accomplishment than male neuropsychiatrists. Seniority was not a strong predictor, but early-career professionals, particularly residents, exhibited higher susceptibility to emotional exhaustion. Perceived job meaning and organizational support were protective factors across all burnout subscales, buffering the effects of job demands. Conclusions: Burnout risk in Italian pediatric neuropsychiatrists is shaped by both job demands and resources. Addressing work–family conflict, job uncertainty, and time pressure alongside enhancing organizational support and fostering job meaning is crucial to mitigate burnout. Special attention should be given to early-career professionals and female neuropsychiatrists to reduce their emotional exhaustion and improve their well-being. These findings provide valuable insights for developing targeted strategies to improve well-being in this field, ultimately enhancing patient care. Full article

Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data. Full article

The flower, recognized as a very important concept worldwide from the earliest ages, is considered a symbol that expresses emotions and thoughts within the shared understanding of humanity. The flower is also found in literature and, therefore, in poetry as a literary genre, showing its effect in many aspects of life. The shaping of the Turkish poetry map has become much more intense, especially during the Republican Period. The existence of movements that set out with different understandings proves this. The Second New movement which took shape in the mid-1950s is one of them. Cemal Süreya was a prominent poet in the Second New movement in Turkish poetry. Like many other poets, Süreya expresses sentiments about life, women, ideas and political identity by blending flowers with humor and his intellectual and versatile personality. The rose is one of the flowers the poet uses in this context. This article presents a different perspective on the poet’s work by examining the specific poems and verses in which the rose is utilized, detailing the frequency and the manner of its application, and analyzing these. Full article

Veterinarians face ethical challenges during their careers, but despite evidence of work-related stress and burnout in veterinarians, moral distress has been poorly investigated. Using an online survey, professional experience, moral distress, and burnout were investigated in 704 Italian veterinarians caring for companion animals, farm animals, or both. The reliability of the moral distress questionnaire was optimal, and Factor analysis identified four factors for moral distress: I. Conflict with owner/farmer; II. Relationships with colleagues/superiors; III. Legal context/unsupportive Professional Institutions; IV. Unshared requests for euthanasia. Veterinarians reported rather low levels of moral distress but high levels of work-related stress/anxiety and a decline in empathy over time, mainly toward owners/farmers. More than 50% of veterinarians reported medium/high levels of burnout, and client-related burnout was significantly higher than animal-related burnout. Levels of work-related stress, moral distress, and burnout were higher in women, and experience exerted a protective effect. Moral distress was positively correlated with emotional exhaustion and depersonalization and, to a lesser extent, lower professional fulfillment. The relatively low level of moral distress that emerged in the study needs further investigation, especially considering the high levels of work-related stress and anxiety reported by veterinarians. Women’s greater sensitivity to moral distress and burnout should not be overlooked given the progressive feminization of the veterinary profession worldwide. Full article

The rising prevalence of depression, with its associated suicide risk, demands effective fast-acting treatments. Ketamine has emerged as promising, demonstrating rapid antidepressant effects. While early studies show swift mood improvements, its precise mechanisms remain unclear. This article aims to compile and synthesize the literature on ketamine’s molecular actions. Ketamine primarily works by antagonizing NMDA receptors, reducing GABAergic inhibition, and increasing glutamate release. This enhanced glutamate activates AMPA receptors, triggering crucial downstream cascades, including BDNF-TrkB and mTOR pathways, promoting synaptic proliferation and regeneration. Moreover, neuroimaging studies have demonstrated alterations in brain networks involved in emotional regulation, including the Default Mode Network (DMN), Central Executive Network (CEN), and Salience Network (SN), which are frequently disrupted in depression. Despite the promising findings, the literature reveals significant inaccuracies and gaps in understanding the full scope of ketamine’s therapeutic potential. For instance, ketamine engages with opioid receptors, insinuating a permissive role of the opioid system in amplifying ketamine’s antidepressant effects, albeit ketamine does not operate as a direct opioid agonist. Further exploration is requisite to comprehensively ascertain its safety profile, long-term efficacy, and the impact of genetic determinants, such as BDNF polymorphisms, on treatment responsiveness. Full article

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements. Emotion detection should be done with fast, discrete, and non-luxurious systems working in real time and real life (wearable devices, wireless communications, battery-powered). Furthermore, emotional reactions to violence are not equal in all people. Then, large general models cannot be applied to a multi-user system for people protection, and health and social workers and law enforcement agents would welcome customized and lightweight AI models. These semi-personalized models will be applicable to clusters of subjects sharing similarities in their emotional reactions to external stimuli. This customization requires several steps: creating clusters of subjects with similar behaviors, creating AI models for every cluster, continually updating these models with new data, and enrolling new subjects in clusters when required. An initial approach for clustering labeled data compiled (physiological data, together with emotional labels) is presented in this work, as well as the method to ensure the enrollment of new users with unlabeled data once the AI models are generated. The idea is that this complete methodology can be exportable to any other expert systems where unlabeled data are added during in-field operation and different profiles exist in terms of data. Experimental results demonstrate an improvement of 5% in accuracy and 4% in F1 score with respect to our baseline general model, along with a 32% to 58% reduction in variability, respectively. Full article

Currently, there is a growing trend for college and university teachers to use AI in their teaching work. However, existing research explores the impact of teachers’ usage of AI in the workplace on students. Based on resource preservation theory, this study examined the mechanism of the usage of AI in teaching on students’ innovative behavior and well-being with a sample of 356 college students from Zhejiang Province. The study found that the usage of AI in teaching significantly and positively affected students’ innovative behavior and well-being, with students’ positive emotion playing a mediating role. Students’ AI trust not only moderated the effect of the usage of AI in teaching on positive emotion, but also moderated the mediating role of positive emotion. The findings have important implications for teachers’ instructional management and practice. Full article

Based on the literature-established benefits of prefabrication to promote better working conditions and its potential to enhance workers’ overall mental health, this study compares the state of mental health of traditional and prefabricated construction workers. In addition to the mostly adopted negative measures of mental health such as anxiety and depression, positive mental health indicators were also used to measure and compare overall mental health conditions of the two categories of construction workers. Data were collected with a questionnaire from 93 prefabricated factory-based and site-based construction workers in Australia. The survey contains eight items to measure poor mental health and seventeen variables of four sub-constructs of positive mental health (emotional, social, psychological, and cultural/religious well-beings). An independent samples t-test was employed to examine the significance of the differences between the means of traditional and prefabricated construction workers on the measured variables and their underlying constructs of mental health and well-being. The findings revealed significantly lower symptoms of burn-out (reduced task accomplishment), suicide ideation (feeling less interested in life), and depression among the workers in prefabricated projects. Similarly, prefabrication tends to provide a positive-mental-health-supportive environment, as the workers were found to be significantly healthier than traditional construction workers on the subjective measures of emotional, social, psychological, and cultural/religious well-beings. Assessing the predictive influence of prefabrication on the overall mental health of different groups of construction workers, especially with qualitative data from different samples in different times, is recommended for future studies. Full article

Background: This research project examining the moderating role of the Scout Movement in supporting mental health through the shaping of personal competence is based on Bandura’s conception of social development (social cognitive theory) in terms of generating a sense of general self-efficacy. Methods: This research examined the moderating value of Scouting with regard to the connection between self-esteem and a sense of efficacy and styles of coping with stress in a group of 683 volunteers. Results: The results suggest that Scouting is a moderator of the relationship between the intensity of an emotion-focused stress coping style and a sense of self-efficacy—being a Scout intensifies the blocking effect of self-esteem on emotions in stressful situations, which can positively influence emotion regulation. Conclusions: The features described suggest the need to research Scouting as a non-formal education strategy to support the development of young people’s mental health in different theoretical and methodological contexts. This work provides conclusions regarding understanding the role of Scouting as a moderator in coping with stress and, consequently, ensuring good mental health. It detailed the knowledge pertaining to specific mechanisms thanks to which Scouting can influence the development of emotional regulation and adaptive response to stressful situations. Full article

The purpose of this paper is to construct and validate a model centered on architectural emotion, to explore the role of emotion in architectural design, and to provide theoretical support for emotion-oriented design. This study collected 614 terms related to architectural emotion, screened 30 core terms, constructed a two-dimensional architectural emotion model, and verified the scientific and practicality of the model through three measurement methods. First, the one-dimensional scale analysis identified two dimensions of pleasure and charm, which portrayed the range of word variation; second, the Principal Component Analysis confirmed the periodic ordering pattern of words, which revealed its systematic relationship; and, lastly, the Multidimensional Scaling Analysis demonstrated the distribution of emotion words based on cognitive similarity in the multidimensional space. Based on this model, this paper proposes a three-layer circular model of “architectural emotion-architectural cognition-architectural elements”, which constructs the correspondence between architectural emotion and design elements, as well as how architectural cognition and architectural elements can synergize to create a spatial experience that triggers specific emotions. The model provides theoretical support for emotion-oriented architectural design and evaluation, and helps designers to better understand the relationship between emotion and space so as to create more valuable architectural works. Full article