Search Results (3,781)

Young women with and without disabilities in South Africa experience challenges accessing sexual reproductive health and rights (SRHR) services, and this may increase during a crisis. We conducted a longitudinal cohort study with 72 young women with and without disabilities (18–25 years) in eThekwini, South Africa (2020–2022) via a series of in-depth interviews including quantitative and qualitative data on participants’ experiences during the COVID-19 pandemic and access to SRHR. Participants reported that barriers to accessing SRHR services included lockdown regulations, prioritization of COVID-19 at health care facilities, fear of COVID-19 infection, transport challenges, and youth-unfriendly clinics. Participants with disabilities experienced additional barriers to SRHR services, including ongoing (inaccessible services) and pandemic-specific (e.g., masks making lipreading impossible) barriers. Participants reported both non-partner and partner violence, with women with disabilities reporting this more frequently, physical and sexual partner violence, as well as physical and emotional abuse from caregivers. Participants with disabilities were not reporting incidents of violence to caregivers or officials because they had ‘normalized’ the experience of violence, were not believed when trying to disclose, feared that reporting would increase their problems, or could not access services due to disability-related barriers. Inclusive and accessible SRHR information, education, and services are needed. This includes disability-specific staff training, disability audits, and caregiver support and training. Full article

Background: Posterior fossa syndrome (PFS), also known as cerebellar mutism syndrome, occurs in about 25% of pediatric patients undergoing resection of a posterior cranial fossa medulloblastoma. It is characterized primarily by mutism or reduced/impaired speech and may include variable symptoms such as motor dysfunction (apraxia, ataxia, hypotonia), supranuclear cranial nerve palsies, neurocognitive changes, and emotional lability. Long-term multidisciplinary rehabilitation is typically required, with recovery taking approximately six months, though many children experience long-term residual deficits. Neuropathic pain associated with PFS is rarely reported in pediatric patients, and evidence for its management is limited. Methods: This case report describes a 10-year-old boy who developed PFS following incomplete resection of a medulloblastoma. Clinical presentation included mutism, irritability, emotional lability, sleep disturbances, and neuropathic pain localized at the C5 level. The patient was treated with a combination of gabapentin, diazepam, and baclofen. Results: The combined pharmacological approach resulted in successful management of the patient’s neuropathic pain and other symptoms associated with PFS, improving his overall condition. Conclusions: This case highlights the potential effectiveness of a multimodal pharmacological regimen for treating neuropathic pain and associated symptoms in pediatric patients with PFS. Further research is needed to explore optimal treatment strategies for this rare but challenging complication. Full article

This study aims to address the gap in understanding condom use (CU) behavior in Flanders (the Dutch-speaking community in Belgium) by applying a mixed methods approach, integrating both quantitative and qualitative data. Utilizing a large-scale survey of over 14,000 participants and 11 in-depth interviews, the study explores key factors influencing CU, including (amongst others) relationship status, attitudes toward condoms, and STI testing. Quantitative findings highlight significant predictors such as the type of partner (casual vs. steady), STI testing behaviors, and negative attitudes toward condoms. Qualitative insights further reveal personal experiences that complicate CU decisions, such as the disruption of sexual flow and emotional dynamics within relationships. These findings underscore the complexity of CU behavior, showing that practical barriers (e.g., discomfort, fit) and personal beliefs play pivotal roles. The study concludes that targeted public health interventions could focus on improving condom accessibility and addressing both practical and emotional factors. Recommendations for sexual health education include promoting communication around CU and enhancing condom experimentation and fit. These findings contribute valuable insights into enhancing sexual health outcomes through more nuanced, contextualized approaches to condom use. Full article

The objective of this study is to integrate and classify electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) signals from a participant exposed to emotional stimuli—happiness, anger, fear, and sadness. We used the LazyPredict library to identify the most effective classification model, leveraging its simplified implementation and wide range of models and performance metrics. The signals were processed in Python following a detailed workflow: (1) normalization, (2) band-pass filtering, (3) epoch extraction and selection, and (4) relative energy extraction using Discrete Wavelet Transform (DWT). After preprocessing, the data were input into LazyPredict, where the Extra Trees model consistently demonstrated the best performance for binary emotion classification. Our experience with LazyPredict proved to be practical and efficient, facilitating the exploration of high-performing models for emotion classification. Full article

This study introduces an integrative framework for association mining within the Chinese battery electric vehicle market, aiming to reveal key user experience (UX) factors and their interrelationships through multidimensional analysis. Utilizing latent Dirichlet allocation (LDA), the study discerned primary themes from user-generated content (UGC). The entropy weight method categorized level 2 factors, while domain-adaptive sentiment analysis quantified emotional responses to BEV user experience dimensions, highlighting significant sentiment disparities among competitors. Co-occurrence network analysis deepened insights into the emotional fabric of UX by exploring tertiary factor associations. Theoretically, this study advances a novel framework informed by Norman’s UX theory, integrating analytical techniques to capture the complexity of UX. Practically, it delivers strategic guidance for BEV manufacturers by analyzing emotional polarities and attribute associations, guiding product innovation and responding to market dynamics. The empirical evidence corroborates the framework’s efficacy in revealing the emotional associations within BEVUX factors, offering valuable implications for both theoretical development and practical application. Full article

This study proposes a system for analyzing non-face-to-face counseling data using text-mining techniques to assess psychological states and automatically classify them into predefined categories. The system addresses the challenge of understanding internal issues that may be difficult to express in traditional face-to-face counseling. To solve this problem, a counseling management system based on text mining was developed. In the experiment, we combined TF-IDF and Word Embedding techniques to process and classify client counseling data into five major categories: school, friends, personality, appearance, and family. The classification performance achieved high accuracy and F1-Score, demonstrating the system’s effectiveness in understanding and categorizing clients’ emotions and psychological states. This system offers a structured approach to analyzing counseling data, providing counselors with a foundation for recommending personalized counseling treatments. The findings of this study suggest that in-depth analysis and classification of counseling data can enhance the quality of counseling, even in non-face-to-face environments, offering more efficient and tailored solutions. Full article

Drawing on methods and theories from the history of emotions, this paper examines the Twin Stelae that flank the entrance into Ramses II’s Great Temple at Abu Simbel in order to investigate the feelings associated with ancient Egyptian kingship from an ideological perspective. As the ruler, what was the king himself supposed to feel, and what feelings was he meant to elicit in his subjects? How did the feelings of the king differ from those of his subjects, and how did all these feelings reinforce and reify the institution of kingship and royal power? In order to propose some answers to these complex questions, I offer a close reading of key words and passages on the Stelae, considering the choice of hieroglyphic signs that the artists used to write them, the ways in which the artists depicted these signs, and the context of the words and passages within the inscriptions. I then use the Stelae’s text to consider how ancient viewers were meant to see and experience the monumental façade of the Great Temple. Full article

The conclusions drawn from commonly used topic modeling and sentiment analysis of COVID-19 vaccination discussions on social media often hinge on researchers’ interpretation. These methods inadequately capture the nuanced real-world human emotions and struggle with identifying sarcasm and handling mixed sentiments. This study uses OpenAI API and its Large Language Models (LLM) to analyze tweets to further the discussion on improving vaccination literacy and fostering public trust. We employed LLM to uncover underlying topics associated with non-polarized sentiments to understand public concerns and factors eroding public confidence in vaccination. In addition, the city and regional level analysis provides a more detailed breakdown of spatial differences in the physical realm. Our results showed a blend of positive sentiments toward COVID-19 vaccination in New York State, with an underlying sense of concern. Our topic analysis reveals that social media platforms, which facilitate personal experience sharing, can influence both vaccination acceptance and hesitancy in positive and negative ways. Our method was able to better capture the nuanced emotions of real-world individuals. This approach is less subjective and more consistent than traditional models as it employs ChatGPT’s extensive pre-trained databases instead of relying on individual researchers’ judgments. Full article

Introduction: A large proportion of the existing voluminous disaster mental health research literature represents the quantitative study of psychopathology, especially posttraumatic stress disorder. Subjective disaster experience is relatively unexplored. Qualitative narratives of surviving a disaster may provide insight into individual experiences of it and efforts to derive meaning from it. Methods: From an initial random sample of 182 survivors of the Oklahoma City bombing, narrative descriptions of this experience were collected 7 years after the bomb blast from 116 of the original sample, for the purpose of examining persistent as well as newly evolving content through qualitative analysis. The narrative content was analyzed for the evolution of thematic content in narrative data also collected at 6 months post-disaster and 1 year later. Results: The thematic content of the bombing experience was structured in a chronological fashion from the bomb blast (sensory, cognitive, and emotional), its immediate aftermath (e.g., escaping danger), and later experiences, (e.g., leaving the bomb site and receiving hospital treatment). During the time between interviews, the focus and general content of the narratives changed minimally, despite considerable compression of detail. Conclusions: The consistency of the material in these narratives over 7 years may reflect the persistence and salience of disaster memories, with the potential for its continuation for the rest of their lives. Full article

Emotion recognition enables machines to more acutely perceive and understand users’ emotional states, thereby offering more personalized and natural interactive experiences. Given the regularity of the responses of brain activity to human cognitive processes, we propose a powerful and novel dual-stream multi-level graph convolution network (DMGCN) with the ability to capture the hierarchies of connectivity between cerebral cortex neurons and improve computational efficiency. This consists of a hierarchical dynamic geometric interaction neural network (HDGIL) and multi-level feature fusion classifier (M2FC). First, the HDGIL diversifies representations by learning emotion-related representations in multi-level graphs. Subsequently, M2FC integrates advantages from methods for early and late feature fusion and enables the addition of more details to final representations from EEG samples. We conducted extensive experiments to validate the superiority of our model over numerous state-of-the-art (SOTA) baselines in terms of classification accuracy, the efficiency of graph embedding and information propagation, achieving accuracies of 98.73%, 95.97%, 72.74% and 94.89% for our model as well as increases of up to 0.59%, 0.32%, 2.24% and 3.17% over baselines on the DEAP-Arousal, DEAP-Valence, DEAP and SEED datasets, respectively. Additionally, these experiments demonstrated the effectiveness of each module for emotion recognition tasks. Full article

Human emotions are an element of attention in various areas of interest such as psychology, marketing, medicine, and public safety. Correctly detecting human emotions is a complex matter. The more complex and visually similar emotions are, the more difficult they become to distinguish. Making visual modifications to the faces of people in photos in a way that changes the perceived emotion while preserving the characteristic features of the original face is one of the areas of research in deepfake technologies. The aim of this article is to showcase the outcomes of computer simulation experiments that utilize artificial intelligence algorithms to change the emotions on people’s faces. In order to detect and change emotions, deep neural networks discussed further in this article were used. Full article

Currently, sports venues are endowed with the important functions of providing sports venues and facilities to the urban public, watching sports events, publicizing the mass sports culture, etc. An in-depth study of how to enhance the attractiveness of sports venue buildings and the competitiveness of the industry to stimulate the public’s potential for sports consumption is necessary. Based on the perspective of color psychology, this study constructs a three-level structural equation model based on the nested data obtained from the questionnaire survey to explore the relationship between managers’ innovative sports venue colors, athletes’ perceived emotional value, and consumers’ sports venue satisfaction. The results show that innovative sports venue colors have a significant positive effect on consumers’ satisfaction with sports venues, and the perceived emotional value has a positive mediating role in the effect of innovative sports venue colors on consumers’ satisfaction with sports venues. This study aims to provide a basis for sports venue managers or event organizers to improve athlete satisfaction, optimize consumer experience, stimulate residents’ motivation to attend games, and strengthen the profitability of the venues. Full article

Crowdfunding videos have become a crucial tool for entrepreneurs seeking financial support, with audio design playing a critical role in attracting potential investors. However, research on how audio features influence crowdfunding success remains limited. This study uses audio analysis techniques to examine data from 4500 crowdfunding campaigns on the Kickstarter platform between 2013 and 2016, investigating the impact of audio features on crowdfunding success rates. Grounded in the signaling theory, we posited four hypotheses suggesting that speech rate, loudness, pitch, and emotional arousal would each exhibit an inverted U-shaped relationship with crowdfunding success rates. Through data analysis, we found that moderate levels of speech rate, loudness, pitch, and emotional arousal significantly enhanced crowdfunding success, whereas extremes in these vocal characteristics had a detrimental effect. Our findings not only extend the application of audio analysis in the crowdfunding domain, but also provide empirical evidence for the influence of audio features on crowdfunding success. This research offers practical guidance for project initiators in developing promotional strategies and for platforms in optimizing user experience. Full article

It is increasingly recognized that interoceptive ability, the capacity to detect, interpret, and consciously integrate signals related to the physiological condition of the body, is central to emotion experience and regulation. Interoceptive ability can be trained and improved through mind–body interventions. This article attempts to provide an integrative review of the link between interoceptive ability and emotion regulation in mind–body interventions. To this aim, (1) we address the constructs of interoceptive ability and mind–body interventions in relation to the double pathway of emotion regulation, and (2) we include a review of selected empirical and qualitative studies. These show that mindfulness meditation affects the brain–body axis through top-down processing, improving both interoceptive ability and emotion regulation. Interventions based on bottom-up processing through body movement and emotional expression are illustrated, but it is argued that they are still under-investigated. In light of the literature reviewed, we contend that interoceptive ability is a crucial aspect associated with the effects of mind–body interventions on emotion regulation. Additionally, we suggest that if studied through both quantitative and qualitative methods, interoceptive ability may serve as a general construct that allows a more integrated view of the polarities related to the spectrum of embodied experience: top-down and bottom-up emotion processing, observational and non-observational body awareness, conscious and unconscious level of interoception. Full article

Youth mental health concerns, including substance abuse, continue to rise. With high co-morbidity rates and a marked lack of representation from diverse groups in study conceptualization, measurement, and implementation, efforts to understand factors impacting youth mental health from a cultural lens are needed. The theory of emotionality stigma posits that many mental health concerns can be understood based on one’s endorsement of emotionality stigma—the experience of stigma around emotions—which manifests within one’s context. Informed by this theory, the current study aimed to adapt and test a measure of emotionality stigma for diverse youth in combined mental health and substance use treatment. Targeted youth focus groups informed the adaptation of the pre-existing Emotionality Stigma Scale for implementation with diverse youth. Using a mixed methods approach, this measure was then tested for relevance, reliability, and validity in an outpatient youth clinic. Patients (N = 58, aged 13 to 21) reported their emotionality stigma, values, and attachment as part of routine monitoring. Based on qualitative feedback and quantitative analysis, our results illustrate the reliability and validity of the adapted Emotionality Stigma Scale and the relevance of this new measure for assessing youth mental health concerns and treatment outcomes. Areas for continued research are identified, and recommendations for implementation in conceptualization and treatment are provided. Full article