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People hold different anti-vaccination attitudes. Having an understanding of how these attitudes have changed prior to and during the COVID-19 pandemic is critical for combatting anti-vaccination attitudes and increasing vaccine intention and uptake. Data were collected from different samples in the United States at three time points prior to (n = 840) and four time points during (n = 1543) the pandemic. All participants completed a multi-dimensional measure of anti-vaccination attitudes (VAX Scale) through an online platform. Results showed that, when it comes to vaccines, worries about unforeseen side effects, concerns about commercial profiteering, and preference for natural immunity were higher during the COVID-19 pandemic than they were prior to it. However, mistrust of vaccine benefit was lower during the COVID-19 pandemic than prior to it. These differences were found even after controlling for the potential effects of participant sex, education, socioeconomic status, age, and race/ethnicity. Additionally, worries about unforeseen side effects, concerns about commercial profiteering, and preference for natural immunity increased linearly alongside the persistence of COVID-19; whereas, mistrust of vaccine benefit showed no change. Although it might be intuitive to emphasize vaccines' effectiveness to increase uptake, the public's trust in vaccine effectiveness did not appear to be the major concern. Thus, public health efforts to increase uptake of vaccines should also focus on reducing concerns about potential side effects from the vaccine, tempering the attention on financial benefits to pharmaceutical companies, and rebuffing the overreliance on natural immunity., Author(s): Ho Phi Huynh [sup.1] Author Affiliations: (1) https://ror.org/0084njv03, grid.469272.c, 0000 0001 0180 5693, Texas A&M University - San Antonio, , One University Way, 78224, San Antonio, TX, USA As [...]

Objectives: The objective of this study was to characterize mental health issues among Vietnamese healthcare workers (HCWs) and to identify related factors. Methods: A cross-sectional study was conducted with 990 HCWs in 2021. Their mental health status was measured using the Depression, Anxiety, and Stress Scale. Results: In total, 49.9%, 52.3%, and 29.8% of respondents were found to have depression, anxiety, and stress, respectively. The multivariable linear regression model revealed that factors associated with increased anxiety scores included depression scores (β, 0.45; 95% confidence interval [CI], 0.39 to 0.51) and stress scores (β, 0.46; 95% CI, 0.41 to 0.52). Factors associated with increased depression scores included being frontline HCWs (β, 0.57; 95% CI, 0.10 to 1.10), stress scores (β, 0.50; 95% CI, 0.45 to 0.56), and anxiety scores (β, 0.41; 95% CI, 0.36 to 0.47), while working experience was associated with reduced depression scores (β, -0.08; 95% CI, -0.16 to -0.01). Factors associated with increased stress scores included working experience (β, 0.08; 95% CI, 0.00 to 0.16), personal protective equipment interference with daily activities (β, 0.55; 95% CI, 0.07 to 1.00), depression scores (β, 0.54; 95% CI, 0.48 to 0.59), and anxiety scores (β, 0.45; 95% CI, 0.39 to 0.50), while age was associated with reduced stress scores (β, -0.12; 95% CI, -0.20 to -0.05). Conclusions: Specific interventions are necessary to enhance and promote the mental health of HCWs so they can successfully cope with the circumstances of the pandemic.

This work addresses Emotion Recognition in Conversation (ERC), a task with substantial implications for the classification of the underlying emotions in spoken encounters. Our focus is on utilizing a fully connected directed acyclic graph to represent conversations, presenting inter-locutor and intra-locutor ties to capture intricate relationships. Therefore, we propose a novel methodology, Residual Relation-Aware Attention (RRAA) with Positional Encoding, enhancing speaker relations’ contexts for improved emotion recognition in conversation. The purpose of this mechanism is to facilitate a thorough comprehension of the connections between speakers, hence enhancing the sophistication and contextual awareness of an emotion recognition framework. We utilized the Gated recurrent units (GRU) to regulate context transmission, ensuring adaptability to changing emotional dynamics. It regulates the transmission of conversation context across all layers of the graph, guaranteeing a flexible and responsive representation of the changing emotional dynamics within the discourse. Evaluations on IEMOCAP, MELD, and EmoryNLP datasets disclose our model’s superior performance (F1 scores: 69.1%, 63.82%, 39.85%, respectively), outperforming state-of-the-art approaches. In general, this work enhances speaker interactions by utilizing a fully connected graph, and providing a more concise and efficient ERC framework.

We present the Group Cohesion and Emotion (GCE) dataset, which comprises 1029 segmented films sourced from YouTube. These videos encompass a range of interactions, including interviews, meetings, informal discussions, and other similar contexts. In the annotation process, graduate psychology students were tasked with assigning coherence levels, ranging from 1 to 7, and affective states as negative, neutral, or positive for each 30 s film. We introduce a foundational model that utilizes advanced visual and audio embedding techniques to investigate the concepts of group cohesion and group emotion prediction. The application of Multi-Head Attention (MHA) fusion is utilized to enhance the process of cross-representation learning. The scope of our investigation includes both unimodal and multimodal techniques, which provide insights into the prediction of group cohesion and the detection of group emotion. The results emphasize the effectiveness of the GCE dataset in examining the level of group unity and emotional conditions.

Objectives The coronavirus disease 2019 (COVID-19) pandemic has increased the workload of healthcare workers (HCWs), impacting their health. This study aimed to assess sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and identify factors associated with poor sleep among HCWs in Vietnam during the COVID-19 pandemic. Methods In this cross-sectional study, 1000 frontline HCWs were recruited from various healthcare facilities in Vietnam between October 2021 and November 2021. Data were collected using a 3-part self-administered questionnaire, which covered demographics, sleep quality, and factors related to poor sleep. Poor sleep quality was defined as a total PSQI score of 5 or higher. Results Participants’ mean age was 33.20±6.81 years (range, 20.0–61.0), and 63.0% were women. The median work experience was 8.54±6.30 years. Approximately 6.3% had chronic comorbidities, such as hypertension and diabetes mellitus. About 59.5% were directly responsible for patient care and treatment, while 7.1% worked in tracing and sampling. A total of 73.8% reported poor sleep quality. Multivariate logistic regression revealed significant associations between poor sleep quality and the presence of chronic comorbidities (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.17 to 5.24), being a frontline HCW directly involved in patient care and treatment (OR, 1.59; 95% CI, 1.16 to 2.16), increased working hours (OR, 1.84; 95% CI,1.37 to 2.48), and a higher frequency of encountering critically ill and dying patients (OR, 1.42; 95% CI, 1.03 to 1.95). Conclusions The high prevalence of poor sleep among HCWs in Vietnam during the COVID-19 pandemic was similar to that in other countries. Working conditions should be adjusted to improve sleep quality among this population.

Abstract Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer’s disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ( $$83.19\%$$ 83.19 % ). Moreover, our performance was equivalent to that of contemporary research.

Even without hearing or seeing individuals, humans are able to determine subtle emotions from a range of indicators and surroundings. However, existing research on emotion recognition is mostly focused on recognizing the emotions of speakers across complete modalities. In real-world situations, emotion reasoning is an interesting field for inferring human emotions from a person’s surroundings when neither the face nor voice can be observed. Therefore, in this paper, we propose a novel multimodal approach for predicting emotion from missing one or more modalities based on attention mechanisms. Specifically, we employ self-attention for each unimodal representation to extract the dominant features and utilize the compounded paired-modality attention (CPMA) among sets of modalities to identify the context of the considered individual, such as the interplay of modalities, and capture people’s interactions in the video. The proposed model is trained on the Multimodal Emotion Reasoning (MEmoR) dataset, which includes multimedia inputs such as visual, audio, text, and personality. The proposed model achieves a weighted F1-score of 50.63% for the primary emotion group and 42.7% for the fine-grained one. According to the results, our proposed model outperforms the conventional approaches in terms of emotion reasoning.

Abstract Spin current, converted from charge current via spin Hall or Rashba effects, can transfer its angular momentum to local moments in a ferromagnetic layer. In this regard, the high charge‐to‐spin conversion efficiency is required for magnetization manipulation for developing future memory or logic devices including magnetic random‐access memory. Here, the bulk Rashba‐type charge‐to‐spin conversion is demonstrated in an artificial superlattice without centrosymmetry. The charge‐to‐spin conversion in [Pt/Co/W] superlattice with sub‐nm scale thickness shows strong W thickness dependence. When the W thickness becomes 0.6 nm, the observed field‐like torque efficiency is about 0.6, which is an order larger than other metallic heterostructures. First‐principles calculation suggests that such large field‐like torque arises from bulk‐type Rashba effect due to the vertically broken inversion symmetry inherent from W layers. The result implies that the spin splitting in a band of such an ABC‐type artificial SL can be an additional degree of freedom for the large charge‐to‐spin conversion.

This study compared the quality of survey data collected from Mechanical Turk (MTurk) workers and college students. Three groups of participants completed the same survey. "MTurk" respondents completed the survey as paid workers using the Mechanical Turk crowdsourcing platform. "Student Online" respondents also completed the survey online after having been recruited in class. Finally, "Student Paper-and-Pencil" respondents completed the survey on paper in a classroom setting. Validity checks embedded in the survey were designed to gauge participants' haste and carelessness in survey completion. MTurk respondents were significantly more likely to fail validity checks by contradicting their own answers or simply completing the survey too quickly. Student groups showed fewer careless mistakes and longer completion times. The MTurk sample tended to be older, more educated, and more ethnically diverse than student samples. Results suggest that researchers should pay special attention to the use of validity checks when recruiting MTurk samples.

Multiple sources of noise can impair the machine’s capacity to learn approximate ground truth in the case of emotion recognition for wild input signals with high variations. Numerous research have been conducted on directly identifying characters’ affective expressions via face, speech, and text. However, there are few studies on the prediction of a character’s emotions based on the content they watch. As a result, in this paper, we propose a hybrid fusion model termed deep graph fusion for predicting viewers’ elicited expressions from videos by leveraging the combination of visual-audio representations. The proposed system is comprised of four stages. To begin, we extract features for each 30-second segment’s visual and auditory modalities using CNN-based pre-trained models to understand their salient representations. Then, we reconstitute these characteristics as graph outlines and use graph convolutional networks to perform node embedding. In the third phase, we offer several fusion modules for combining visual and auditory branch graph representations. Finally, the fused features are utilized to estimate the evoked scores for all emotional classes using Sigmoid activation. Additionally, we present a semantic embedding loss to understand the semantic meaning of textual emotions in order to improve overall performance. We evaluate the proposed method using the Evoked Expression from Videos (EEV) database on both the validation and test sets. The experimental results demonstrate that the proposed algorithm outperforms all conventional models.

ObjectiveCultural portrayals of physicians suggest an unclear and even contradictory role for humility in the physician-patient relationship. Despite the social importance of humility, however, little empirical research has linked humility in physicians with patient outcomes or the characteristics of the doctor-patient visit. The present study investigated the relationship between physician humility, physician-patient communication, and patients' perceptions of their health during a planned medical visit.MethodsPrimary care physician-patient interactions (297 patients across 100 physicians) were rated for the physician's humility and the effectiveness of the physician-patient communication. Additionally, patients reported their overall health and physicians and patients reported their satisfaction with the interaction.ResultsWithin-physician fluctuations in physician humility and self-reported patient health positively predicted one another, and mean-level differences in physician humility predicted effective physician-patient communication, even when controlling for the patient's and physician's satisfaction with the visit and the physician's frustration with the patient.ConclusionsThe results suggest that humble, rather than paternalistic or arrogant, physicians are most effective at working with their patients.Practice implicationsInterventions to improve physician humility may promote better communication between health care providers and patients, and, in turn, better patient outcomes.

ContextGood patient-provider interactions promote satisfaction with health care, adherence to treatment recommendations and improved health. However, little research has examined patients' emotions and how they relate to patients' experiences with health care and their adherence intentions in acute care settings.ObjectiveThis study examined the predictors and consequences of two emotions pertinent to the uncertainty of acute health-care experiences: anxiety and hopefulness.DesignPatients who arrived at a general surgery clinic for an initial consultation were interviewed before and after the consultation. Prior to the consultation with a physician, patients completed baseline measures of their emotional state. Following the consultation, patients completed measures of understanding of the information provided by the surgeon, perceived control over treatment decisions, adherence intentions and emotional state.ResultsUnderstanding and control predicted less anxiety and greater hopefulness, compared to baseline. Only hopefulness predicted adherence intentions. These relationships remained even after controlling for characteristics of the patients and interactions.DiscussionThese findings identify aspects of psychosocial care that are critical for promoting positive (and mitigating negative) emotional states in patients. Even in a brief consultation in a clinic setting, physicians may be able to improve patients' emotional state by promoting a sense of control and clarifying information they convey, and patients' positive emotional states may be critical for raising adherence intentions.

Speech emotion recognition is a challenging but important task in human computer interaction (HCI). As technology and understanding of emotion are progressing, it is necessary to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities supporting human decision making and to design human-machine interfaces (HMI) that assist efficient communication. This paper presents a multimodal approach for speech emotion recognition based on Multi-Level Multi-Head Fusion Attention mechanism and recurrent neural network (RNN). The proposed structure has inputs of two modalities: audio and text. For audio features, we determine the mel-frequency cepstrum (MFCC) from raw signals using the OpenSMILE toolbox. Further, we use pre-trained model of bidirectional encoder representations from transformers (BERT) for embedding text information. These features are fed parallelly into the self-attention mechanism base RNNs to exploit the context for each timestamp, then we fuse all representatives using multi-head attention technique to predict emotional states. Our experimental results on the three databases: Interactive Emotional Motion Capture (IEMOCAP), Multimodal EmotionLines Dataset (MELD), and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI), reveal that the combination of the two modalities achieves better performance than using single models. Quantitative and qualitative evaluations on all introduced datasets demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.

Social comparison processes can affect academic and interpersonal outcomes for minoritized college students. However, the literature has not addressed how these processes play out for Hispanic students who attend Hispanic Majority Institutions, in which Hispanic students comprise more than 50% of the population. In this study, such students (N = 191) completed online assessments for social comparison orientation (SCO), belongingness, academic self-concept, and perceived stress. Results showed that, regarding belongingness, SCO was positively correlated to rejection/exclusion, but not acceptance/inclusion. Regarding academic self-concept, SCO was positively correlated to self-doubt, but not self-confidence. Mediation analyses showed that perceived stress fully mediated the relationship between SCO and rejection/exclusion, and partially mediated the relationship between SCO and academic self-doubt. In other words, SCO was associated with increased stress, which was then associated with increased feelings of rejection/exclusion and self-doubt about academic abilities. These findings offer additional insight into Hispanic college students' experience. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop and evaluate a new deep learning architecture, namely regenerative semi-supervised bidirectional W-network (RSS-BW), to predict the tumor state of the knee bone from radiographic images. First, we constructed an autoencoder model, called Bidirectional W-network (BW), for segmenting three-region (i.e., femur, tibia, and fibula) of knee bone. Using these regions as input data, RSS-BW architecture consisting of the autoencoding model for regenerating the bone structures, the backbone model for extracting features with pretrained ImageNet, and the predicting model for knee bone tumor classification are established. The developed scheme rapidly obtained segmentation results of the three-region of knee bone with a mean dice score of 98.06 ± 0.08%. Moreover, two types of classification are conducted: single-step and double-step. The single-step classifies the bone images into normal, benign, and malignant states. The mean values of accuracy, precision, recall and F-beta score obtained from the segmented images were 85.23±3.91%, 82.23±3.22%, 82.15±3.31%, and 82.21±3.25%, respectively. For the double-step of bone tumor classification, the images are classified first as normal versus abnormal. The second classification is conducted for abnormal images as benign versus malignant. The double-step classification shows an improvement of the mean accuracy by 1.7% compared to the single-step classification. In conclusion, the RSS-BW model presents higher accuracy than conventional models, indicating its potential clinical decision support for bone tumor classification.

Test anxiety is common and may lead to a range of negative outcomes, including poor exam performance. Therefore, it is important to explore psychological predictors of test anxiety. In this paper, we examined whether intellectual humility can predict test anxiety. In Study 1, college students (N = 181) completed an intellectual humility measure with four subscales and two different measures of test anxiety. In Study 2 (N = 196), a community sample recruited from an online workforce completed the same measures. In both studies, we found that intellectual humility was negatively related to test anxiety, such that higher intellectual humility predicted lower test anxiety. Specifically, Study 1 demonstrated a negative correlation between intellectual humility and the Sarason Test Anxiety Scale; Study 2 confirmed this negative relationship with both the Sarason and Westside test anxiety scales. We also found that this relationship was largely driven by the intellectual humility subscale of Independence of Intellect and Ego. Additionally, these results were present even when controlling for key demographic factors. These findings highlight intellectual humility's role in predicting exam anxiety and offer a potential avenue for intellectual humility to be leveraged into interventions to decrease exam anxiety in the future. [ABSTRACT FROM AUTHOR]

In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large datasets that are specifically acquired for stress emotion with varying degrees of expression for this task. In this paper, we introduced a dataset for Stress Analysis with Dimensions of Valence and Arousal of Korean Movie in Wild (SADVAW), which includes video clips with diversity in facial expressions from different Korean movies. The SADVAW dataset contains continuous dimensions of valence and arousal. We presented a detailed statistical analysis of the dataset. We also analyzed the correlation between stress and continuous dimensions. Moreover, using the SADVAW dataset, we trained a deep learning-based model for stress recognition.

Though many people loathe celebrities and their lifestyles, celebrities can also be perceived as being available to provide community support. The purpose of this study was to establish the psychometric properties of the Perceived Celebrity Support Inventory. The scale was developed to capture three aspects of perceived celebrity support to the community: instrumental (i.e., providing tangible and concrete assistance), informational (i.e., advice, explanation, and information), and emotional (i.e., empathizing, encouraging, listening, and comforting) support. Factor and item analyses have revealed robust psychometric properties in samples drawn from the United States (US) and Iran. The US sample perceived higher community support from celebrities than the Iranian sample. In the Iranian sample, perceived celebrity support for the community was associated with more positive attitudes and less hatred of celebrity culture. In the US, perceived celebrity support was associated with negative attitudes and elevated hatred towards celebrities. The findings advance the literature by providing an instrument with which to measure positive aspects of celebrity influence across cultures. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has dominated medical image segmentation. Encoder-decoder architectures, such as U-Net, can be used in state-of-the-art models with powerful designs that are achieved by implementing skip connections that propagate local information from an encoder path to a decoder path to retrieve detailed spatial information lost by pooling operations. Despite their effectiveness for segmentation, these naïve skip connections still have some disadvantages. First, multi-scale skip connections tend to use unnecessary information and computational sources, where likable low-level encoder features are repeatedly used at multiple scales. Second, the contextual information of the low-level encoder feature is insufficient, leading to poor performance for pixel-wise recognition when concatenating with the corresponding high-level decoder feature. In this study, we propose a novel spatial-channel attention gate that addresses the limitations of plain skip connections. This can be easily integrated into an encoder-decoder network to effectively improve the performance of the image segmentation task. Comprehensive results reveal that our spatial-channel attention gate remarkably enhances the segmentation capability of the U-Net architecture with a minimal computational overhead added. The experimental results show that our proposed method outperforms the conventional deep networks in term of Dice score, which achieves 71.72%.

Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human−computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human−computer interaction by hand in the future.