Types Of Emotions Research Articles

A systematic review of workplace triggers of emotions in the healthcare environment, the emotions experienced, and the impact on patient safety

BackgroundHealthcare staff deliver patient care in emotionally charged settings and experience a wide range of emotions as part of their work. These emotions and emotional contexts can impact the quality and safety of care. Despite the growing acknowledgement of the important role of emotion, we know very little about what triggers emotion within healthcare environments or the impact this has on patient safety.ObjectiveTo systematically review studies to explore the workplace triggers of emotions within the healthcare environment, the emotions experienced in response to these triggers, and the impact of triggers and emotions on patient safety.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, four electronic databases were searched (MEDLINE, PsychInfo, Scopus, and CINAHL) to identify relevant literature. Studies were then selected and data synthesized in two stages. A quality assessment of the included studies at stage 2 was undertaken.ResultsIn stage 1, 90 studies were included from which seven categories of triggers of emotions in the healthcare work environment were identified, namely: patient and family factors, patient safety events and their repercussions, workplace toxicity, traumatic events, work overload, team working and lack of supervisory support. Specific emotions experienced in response to these triggers (e.g., frustration, guilt, anxiety) were then categorised into four types: immediate, feeling states, reflective, and longer-term emotional sequelae. In stage 2, 13 studies that explored the impact of triggers or emotions on patient safety processes/outcomes were included.ConclusionThe various triggers of emotion and the types of emotion experienced that have been identified in this review can be used as a framework for further work examining the role of emotion in patient safety. The findings from this review suggest that certain types of emotions (including fear, anger, and guilt) were more frequently experienced in response to particular categories of triggers and that healthcare staff's experiences of negative emotions can have negative effects on patient care, and ultimately, patient safety. This provides a basis for developing and tailoring strategies, interventions, and support mechanisms for dealing with and regulating emotions in the healthcare work environment.

Emotion Based Music Player

An emotions-based music player is a software that detects the emotions of an individual with the help of a webcam and then analyses the mood of an individual and then recommends songs that may improve the mood of the user. Music has been proven to show positive effects on the overall mood of the user if the user is in a bad mood. Hence, we may use music to alleviate the mood of the individual. The system will capture an image of the user with the help of a webcam and then analyze the image for the type of emotion. For example, if the image shows an angry man, then the system will suggest appropriate songs to help improve the current mood of the user. We have included and implemented deep learning. This is a crucial part of our project

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Emotion is a human sense that can influence an individual’s life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

The relationships among trait mindfulness, obsessive passion, and emotions in high school sport managers

Purpose Mindfulness emerges as a possible means to alleviate obsessive passion. Nevertheless, its application in sport leadership context remains understudied, despite the significant levels of stress and turnover that are common in this profession. This study aims to investigate the relationships among trait mindfulness, obsessive passion, and specific positive (e.g., happiness), neutral (e.g., calmness), and negative (e.g., anger) emotions in high school sport managers. Method In total, 358 high school sport managers ( Mage = 48.47, SD = 8.93) working in 46 states in the United States completed the online questionnaires measuring the proposed variables. Descriptive statistics, correlation analysis, confirmatory factor analysis, and structural equation modeling were utilized to examine the hypotheses. Result The results showed that trait mindfulness negatively predicted obsessive passion. Additionally, obsessive passion negatively predicted the feelings of happiness and calmness, while it positively predicted anger. The mediation analysis revealed that obsessive passion fully mediated the relationships between trait mindfulness and the three types of emotion. Conclusion These findings are significant because they demonstrate the buffering role of trait mindfulness in obsessive passion. Furthermore, the study provides meaningful insights into sport managers’ emotional experiences, particularly by empirically examining the effect of obsessive passion on different emotions.

ACADEMIC EMOTIONS OF MIDDLE SCHOOL STUDENTS LEARNING ENGLISH AFTER THE COVID-19 PANDEMIC: HIGH ACHIEVERS' EXPERIENCES

Changes in English language learning experienced by students after the pandemic can elicit a range of academic emotions. These emotional changes can be due to the environment and different learning atmospheres. The existence of these emotional changes will also have an impact on students’ academic achievement. Therefore, this study aims to examine the academic emotions of high-achieving students of junior high school learning English after the Covid-19 pandemic and how they affect students’ effectiveness in learning English. Using a descriptive case study research design to explore students' academic emotions, the research revealed the existence of four types of academic emotions: achievement, epistemic, topic, and social emotions. Each type was accompanied by positive and negative experiences. The positive emotions such as happiness, enjoyment and confidence can be caused by their enthusiasm and interest in learning, which closely related to the achievement obtained. Inversely related to the negative emotions, they tend to feel bad when they do not like something done during offline learning.

Comparison and analysis of prediction accuracy between traditional machine learning algorithms and XGBoost algorithm in music emotion classification

Music emotion classification refers to determining the emotional types of music, such as happiness, sadness, anger, and passion, based on aspects like rhythm, melody, and tone. The development of artificial intelligence can be applied to music recommendation by employing machine learning algorithms to ascertain emotional attributes of music, enabling precise music suggestions for users to enhance their musical experience. This paper compares and analyzes the classification performance of traditional Random Forest machine learning algorithms and the XGBoost model on a Turkish music emotion dataset. We selected a comprehensive dataset from Kaggle, containing a vast array of music samples and their corresponding emotional labels, making it an extensive music emotion classification dataset. From the experimental results, it's evident that the traditional Random Forest machine learning model outperforms the XGBoost model in terms of accuracy, precision, and recall. The accuracy of the traditional Random Forest machine learning model stands at 80.8%, whereas the XGBoost model's accuracy is 75%. The recall rate for the traditional Random Forest machine learning model is 80.8%, while for XGBoost, it's 77.2%. The F1 score for the traditional Random Forest machine learning model is 80.5%, whereas for XGBoost, it's 75.3%. These parameters indicate that the traditional Random Forest machine learning model exhibits superior predictive performance in music emotion classification. However, the XGBoost model possesses its own advantages, such as faster learning and prediction speeds, high accuracy, and strong generalization capabilities. In summary, the traditional Random Forest machine learning model demonstrates better robustness and interpretability, effectively handling samples with noise and missing data, thus finding widespread practical application. On the other hand, the XGBoost model excels in rapid training and prediction, coupled with higher accuracy and versatility, making it advantageous in dealing with large datasets. The research outcomes of this paper hold significant importance for the study and application of music emotion classification. The experimental results presented herein offer valuable insights for researchers and practitioners, aiding them in selecting appropriate machine learning models, optimizing, and adjusting them to achieve the best classification results.

The relationship between health-promoting behaviors and negative emotions in college freshmen: a cross-lagged analysis.

The prevalence of mental health issues has been gradually increasing among college students in recent years. Improvements in mental health can be achieved through changes in daily behavior and the use of psychological counseling. This study aims to investigate the relationship between health-promoting behaviors and negative emotions among college freshmen as they enter the university. It also examines the impact of various sub-dimensions of health-promoting behaviors and other factors on the negative emotions (stress, anxiety, and depression) experienced by college freshmen. Using the Negative Emotion and Health-Promoting Behavior scales, a 7-month longitudinal study was conducted on 4,252 college freshmen, with collection of data at two time points (T1: November 12, 2021; T2: June 17, 2022). Out of this longitudinal study, 3,632 valid samples were obtained. This research aimed to explore the association and impact between negative emotions and the level of health-promoting behaviors among college students during their time at the university. ① There were significant differences in the levels of health-promoting behaviors and negative emotions over the course of 7 months (P < 0.05). Health-promoting behaviors were found to have a significant negative correlation with negative emotions (P < 0.05). ② Negative emotions at T1 significantly negatively predicted health-promoting behaviors at T2 (β = -0.11, P < 0.01), while health-promoting behaviors at T1 significantly negatively predicted negative emotions at T2 (β = -0.12, P < 0.001). ③ Stress management (β = -0.104, P < 0.05; β = -0.087, P < 0.05), self-actualization (β = -0.282, P < 0.01; β = -0.260, P < 0.05), health responsibility (β = -0.057, P < 0.05; β = -0.088, P < 0.05), and interpersonal relations (β = 0.068, P < 0.01; β = 0.138, P < 0.05) were important components in improving stress and anxiety. Self-actualization (β = -0.437, P < 0.001), exercise (β = 0.048, P < 0.001), nutrition (β = 0.044, P < 0.001), and interpersonal relations (β = 0.065, P < 0.001) were important components in improving depression. ④ Gender, place of household registration, and whether the individual is the only child were significant factors affecting negative emotions in college freshmen. The level of health-promoting behaviors is an important indicator for assessing the negative emotional states of college freshmen. Enhancing health-promoting behaviors across various dimensions can help alleviate different types of negative emotions. Gender, place of household registration, and being the only child are significant factors that influence negative emotions.

Family Emotional Decorative Paintings: Interaction Design for Communication

This paper describes an approach applied to decorative home paintings: an emotional interaction approach. The study utilizes the theory of constructed emotion and the Wizard of Oz research methodology to extend the user's emotional experience in home scenarios with emotional self-reflection and regulation. Family Emotion Decorative Painting uses the family-shared photo library as the image base data, obtains the emotion type through facial expression and voice emotion recognition, and correlates the moment's emotion with the emotion reacted in the picture. Through qualitative research, we found that family emotional decorative painting can bring emotional arousal and soothing to participants. Family Emotional Decorative Paintings also provoked reflection on technology in and around the home. These findings suggest several opportunities, such as designing for anticipation, better supporting reflection on the past. The study also allowed the team to question the traditional interaction design view that more frequent and prolonged human-computer interactions are a measure of good design. The team will next focus on ways for users to engage in novel and meaningful ways outside of the application.

Principles of Emotional Stability among Muslim Youth and its Educational Applications in the Personality of Prophet Joseph (Yusuf), Peace be Upon Him

Objectives: The aim of this study is to formulate the Islamic principles of emotional stability among Muslim youth, considering them the most important groups in society. This is particularly important due to the contemporary challenges faced by youth today, which intensify their tension and emotions. It was necessary to infer the Islamic principles of emotional stability that teach youth to control their emotions and confront their problems in a balanced manner. Additionally, the aim is to acknowledge the indicators of emotional stability in the Holy Quran and the Sunnah of the Prophet, peace be upon him. Methodology: The study employs a descriptive-analytical approach, involving the examination of Quranic verses and Prophetic sayings containing specific emotions. Subsequently, Islamic principles of emotional stability are formulated, followed by their pedagogical application to the character of Yusuf, peace be upon him. Results: The study concluded that emotional stability is a contemporary necessity for Muslim youth. It demonstrates that emotions have both positive and negative effects on human behavior. Additionally, there are Islamic principles for emotional stability, and it is considered that the prophets are perfect role models in this regard. This helps youth achieve numerous benefits in their daily lives. Summary: There are various types of emotions, and clear indications of emotional stability are mentioned in the Holy Quran and the Sunnah, from which Islamic principles of emotional stability can be inferred. The character of Yusuf, peace be upon him, serves as a pedagogical application for emotional stability in some aspects. Most likely, the results have a positive reflection of emotional stability in the lives of the youth. Recommendations: This study recommends training youth in emotional stability strategies through training courses, workshops, and inclusion of educational curricula addressing content of emotional stability. Furthermore, it suggests guiding graduate students to conduct research on youth emotions and treatment methods.

Can Induced Awe Reduce Anti-Gay Prejudice in Heterosexual Adults and Does the Need for Closure Moderate this Effect?

With sexual prejudice continuing to be widely prevalent and seriously harmful, there is a need to find ways to reduce anti-gay prejudice (AGP). This online experimental study examined if a novel intervention, induced awe, can reduce AGP; if those high in need for closure (NFC) show higher AGP; and if NFC moderates the effect of awe on AGP. In total, 154 heterosexual adults completed the Need for Closure Scale (Roets &amp; Van Hiel, 2011) before being randomly assigned to one of three emotion inducement interventions – 1) watching a 4.43 min long video of the target emotion, awe, 2) watching the comparison emotion, amusement, or 3) watching a neutral emotion as a control. Post intervention, the participants completed the explicit measures Homosexuality Attitudes Scale (HAS; Kite &amp; Deaux, 1986). Data were analysed using a 2 (NFC) x 3 (emotion type) independent factorial ANOVA. None of the three hypotheses were supported since there were no main effects of awe or NFC on AGP, and no interaction effect of awe and NFC on AGP. Key implications of these results were 1) awe inducement does not change prejudicial attitudes at an explicit level (Dale et al., 2020), 2) factors beyond NFC, like Right Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO), may influence the effectiveness of awe in reducing prejudice, and 3) questions were raised about certain boundaries for awe’s effectiveness. Methodological modifications suggested for future research include using implicit measures or veiled elicitation methods for authentic measurement of AGP, employing more potent awe elicitors, and assessing the mediating role of RWA and SDO on the effect of NFC on AGP.

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning.

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants' emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

Linguistic based emotion analysis using softmax over time attention mechanism.

Recognizing the real emotion of humans is considered the most essential task for any customer feedback or medical applications. There are many methods available to recognize the type of emotion from speech signal by extracting frequency, pitch, and other dominant features. These features are used to train various models to auto-detect various human emotions. We cannot completely rely on the features of speech signals to detect the emotion, for instance, a customer is angry but still, he is speaking at a low voice (frequency components) which will eventually lead to wrong predictions. Even a video-based emotion detection system can be fooled by false facial expressions for various emotions. To rectify this issue, we need to make a parallel model that will train on textual data and make predictions based on the words present in the text. The model will then classify the type of emotions using more comprehensive information, thus making it a more robust model. To address this issue, we have tested four text-based classification models to classify the emotions of a customer. We examined the text-based models and compared their results which showed that the modified Encoder decoder model with attention mechanism trained on textual data achieved an accuracy of 93.5%. This research highlights the pressing need for more robust emotion recognition systems and underscores the potential of transfer models with attention mechanisms to significantly improve feedback management processes and the medical applications.

Emotion recognition in amyotrophic lateral sclerosis in a dynamic environment

ObjectiveThe aim of our study was to measure the ability of ALS patients to process dynamic facial expressions as compared to a control group of healthy subjects and to correlate this ability in ALS patients with neuropsychological, clinical and neurological measures of the disease. MethodsSixty-three ALS patients and 47 healthy controls were recruited. All the ALS patients also underwent i) the Geneva Emotion Recognition Test (GERT) in which ten actors express 14 types of dynamic emotions in brief video clips with audio, ii) the Edimburgh Cognitive and Behavioral ALS Screen (ECAS) test; iii) the ALS Functional Rating Scale Revised (ALSFRS-R) and iv) the Medical Research Council (MRC) for the evaluation of muscle strength. All the healthy subjects enrolled in the study underwent the GERT. ResultsThe recognition of irritation and pleasure was significantly different between ALS patients and the control group. The amusement, despair, irritation, joy, sadness and surprise had been falsely recognized differently between the two groups. Specific ALS cognitive impairment was associated with bulbar-onset phenotype (OR = 14,3889; 95%CI = 3,96-52,16). No association was observed between false emotion recognition and cognitive impairment (F(1,60)=,56,971, p=,45,333). The number of categorical errors was significantly higher in the ALS patients than in the control group (27,66 ± 7,28 vs 17,72 ± 5,29; t = 8723; p = 0.001). ConclusionsALS patients show deficits in the dynamic processing of a wide range of emotions. These deficits are not necessarily associated with a decline in higher cognitive functions: this could therefore lead to an underestimation of the phenomenon.

Role of Synchronous, Moderated, and Anonymous Peer Support Chats on Reducing Momentary Loneliness in Older Adults: Retrospective Observational Study.

Older adults have a high rate of loneliness, which contributes to increased psychosocial risk, medical morbidity, and mortality. Digital emotional support interventions provide a convenient and rapid avenue for additional support. Digital peer support interventions for emotional struggles contrast the usual provider-based clinical care models because they offer more accessible, direct support for empowerment, highlighting the users' autonomy, competence, and relatedness. This study aims to examine a novel anonymous and synchronous peer-to-peer digital chat service facilitated by trained human moderators. The experience of a cohort of 699 adults aged ≥65 years was analyzed to determine (1) if participation, alone, led to measurable aggregate change in momentary loneliness and optimism and (2) the impact of peers on momentary loneliness and optimism. Participants were each prompted with a single question: "What's your struggle?" Using a proprietary artificial intelligence model, the free-text response automatched the respondent based on their self-expressed emotional struggle to peers and a chat moderator. Exchanged messages were analyzed to quantitatively measure the change in momentary loneliness and optimism using a third-party, public, natural language processing model (GPT-4 [OpenAI]). The sentiment change analysis was initially performed at the individual level and then averaged across all users with similar emotion types to produce a statistically significant (P<.05) collective trend per emotion. To evaluate the peer impact on momentary loneliness and optimism, we performed propensity matching to align the moderator+single user and moderator+small group chat cohorts and then compare the emotion trends between the matched cohorts. Loneliness and optimism trends significantly improved after 8 (P=.02) to 9 minutes (P=.03) into the chat. We observed a significant improvement in the momentary loneliness and optimism trends between the moderator+small group compared to the moderator+single user chat cohort after 19 (P=.049) and 21 minutes (P=.04) for optimism and loneliness, respectively. Chat-based peer support may be a viable intervention to help address momentary loneliness in older adults and present an alternative to traditional care. The promising results support the need for further study to expand the evidence for such cost-effective options.

Advancing language teacher emotion research: a nuanced, dialectical, and empowering stance

Abstract This commentary presents a nuanced, dialectical, and empowering perspective on research pertaining to language teacher emotions, drawing upon empirical research papers featured in this special issue, as well as other relevant literature in the field of language teacher education. Recognizing the embodied, dynamic, and potentially contested nature of teacher emotional labor, the paper emphasizes the importance of presenting a nuanced portrayal, adopting a dialectical approach, and embracing an empowering mindset when investigating language teacher emotions. It also highlights important directions for future research, advocating for a conceptual shift toward examining teacher emotional labor as a distributed practice with a participatory and transformative orientation. Furthermore, the significance of both “looking inward” to delve into specific types of emotions and their interactions as well as “looking outward” to explore how emotions relate to other crucial dimensions of language teachers’ professional practices such as identity, mindfulness, and resilience, is emphasized.

Effect of emotions on learning, memory, and disorders associated with the changes in expression levels: A narrative review.

Emotions, in general, have no scientific definition. Emotions can be denoted as the mental state because of the neurophysiological changes. Emotions are related to mood, personality, temperament, and consciousness. People exhibit different emotions in different situations causing changes in cognitive functions. One of the major cognitive functions is the ability to learn, to store the acquired information in the parts of the brain such as the hippocampus, amygdala, cortex, and cerebellum. Learning and memory are affected by different types of emotions. Emotional responses such as fear, depression, and stress have impaired effects on cognitive functions such as learning and memory, whereas optimistic and happy emotions have positive effects on long-term memory. Certain disorders have greater effects on the regions of the brain which are also associated with synaptic plasticity and Learning and Memory(LM). Neuroimaging techniques are involved in studying the changing regions of the brain due to varied emotions and treatment strategies based on the changes observed. There are many drugs, and in advancements, nanotechnology is also utilized in the treatment of such psychiatric disorders. To improve mental health and physical health, emotional balance is most important, and effective care should be provided for people with less emotional quotient and different types of disorders to inhibit cognitive dysfunctions. In this review, emotions and their varied effects on a cognitive function named learning and memory, disorders associated with the defects of learning due to emotional instability, the areas of the brain that are in control of emotions, diagnosis, and treatment strategies for psychiatric disorders dependent on emotions are discussed.

Do incidental positive emotions induce more optimistic expectations of decision outcomes? An empirical study from the perspective of event-related potential.

Previous research has found that incidental emotions of different valences (positive/negative/neutral) influence risky decision-making. However, the mechanism of their influence on psychological expectations of decision outcomes remains unclear. We explored the effects of different incidental emotions on the behavioral, psychological, and electrophysiological responses of individuals in risky decision-making through a money gambling task using a one-way (emotion type: positive, negative, neutral emotions) between-subjects experimental design. Individuals with positive emotions had significantly greater risk-seeking rates than those with negative emotions during the decision selection phase (p<.01). In the feedback stage of decision outcomes, individuals showed stronger perceptions of uncertainty in the decision environment under gain and loss feedback compared with neutral feedback, as evidenced by a more positive P2 component (i.e., the second positive component of an event-related potential). Positive emotions produced greater than expected outcome bias than neutral emotions, as evidenced by a more negative FRN component (i.e., the feedback-related negativity component). Our results suggest that positive emotions increase individuals' psychological expectations of decision outcomes. This study provides new empirical insights to understand the influence of incidental emotions on risky decision outcome expectations.

Experiential Avoidance During Mealtimes Among Individuals With Eating Disorders

The relationship between negative emotions and avoidance is widely theorized as a bidirectional cycle implicated in a range of psychopathology. Historically, research on this cycle has examined one type of negative emotion: anxiety. Yet, a broader range of internal experiences may be implicated in the maintenance of unhealthy avoidance cycles in psychopathology. This study examines prospective relationships among anxiety, guilt, physical discomfort, and experiential avoidance during mealtimes for individuals with eating disorders (EDs). Participants (N = 108) completed ecological momentary assessments four times a day for 25 days. We computed multilevel models to examine between- and within-person effects of negative emotions and physical discomfort on experiential avoidance. When including guilt and anxiety in one model, guilt, but not anxiety, explained the significant variance in experiential avoidance at the next meal. Mealtime physical discomfort and experiential avoidance evidenced reciprocal prospective relationships. Future research should test whether interventions targeting experiential avoidance and physical discomfort at mealtimes disrupt guilt.

The impact of clinical context on the recognition of facial expressions

IntroductionSeveral authors have demonstrated the relevance of the therapist sensitivity to the affective expression of his client (Merten &amp; Schwab, 2005; 150-158), as well as to his own emotional experience (Haynal-Raymond et al., 2005;142-148) in order to build a more effective therapeutic relationship, and results. An important source of information to decode the emotional expression hints is the face, and its expression (Ekman &amp; Friesen, 1975; Russel &amp; Fernández-Dolls, 1997;275-294). Despite common sense saying that context is relevant to understand the meaning of the emotional facial expression, the literature review shows inconsistent results.ObjectivesThe main goal of this study was to evaluate the impact of clinical context over the perception of the emotional facial expression.MethodsThis study followed a within-subjects design, and its sample consisted of 60 clinical psychologists. 21 combinations of prototypical expression images with mixed emotional signals, and clinical information texts were presented to the participants. Then their judgement on the type of emotion displayed was requested. The presentation of the text-image pairs was randomized between three conditions: consistent, and non-consistent, and neutral.ResultsThe results suggest that emotions are more easily recognized in the presence of a concordant context than a non-concordant or neutral one, and that the greater the similarity between the facial expression of the image presented and the face prototypically associated with the context, the greater the influence of the context.However, In the recognition of mixed emotional signs, there was greater recognition of signs of anger in the facial expression, as a non-dominant emotion, when in the presence of the neutral story than of the story that agreed with the dominant emotion (sadness). There was also greater recognition of sadness, as a non-dominant emotion, in the presence of a story in agreement with fear than in the presence of a neutral story. There was also a statistically significant increase in the attribution of anger to images in which it is not present and whose dominant emotion is fear, when associated with a context of aggression vs. a neutral context.It was also found that there was a significant decrease in the attribution of fear to the sadness-anger image (25%-75%) in the presence of the aggression context compared to the neutral and panic contexts.There was also a statistically significant decrease in the attribution of sadness to an image of fear in the neutral context compared to the other contexts (panic and aggression).ConclusionsIn conclusion, our study have shown an impact of context over overvaluation or the undervaluation of the emotional facial expression as well as either with prototypical expressions or the mixed emotional signals when referring to sadness, fear, and anger. Thus, mental health clinicians should consider the influence of these contexts.Disclosure of InterestNone Declared

The impact of grandparenting on mental health among rural middle-aged and older adults in China: exploring the role of children's support.

In the rural regions of China, characterized by a pronounced aging demographic and limited resources, a substantial proportion of middle-aged and older adults engage in grandparenting roles. Yet, the literature lacks consistent evidence regarding the effects of grandparenting on the mental health of this cohort. Accordingly, this study aimed to explore the impact of grandparenting on the mental health of rural middle-aged and older adults, as well as the underlying mechanisms. This analysis encompassed 10,881 middle-aged and older adults, utilizing data from the 2018 Harmonized China Health and Retirement Longitudinal Study (CHARLS). The mental health of participants was assessed using the Center for Epidemiological Studies Depression-10 (CESD-10) scale, while support from children was categorized into financial and emotional types. The study employed logistic and OLS regression models to identify the mediating role of child support and utilized the Karlson-Holm-Breen (KHB) method for decomposing this mediating effect. The findings demonstrated that grandparenting had a significant negative impact on depression among rural middle-aged and older adults. Furthermore, children's support played a vital role in mediating this relationship, accounting for approximately one-third of the overall influence. Moreover, the decomposition analysis revealed that both emotional and economic support from adult children equally contributed to the declination of depression among rural middle-aged and older adults. Grandparenting significantly enhances mental well-being in rural middle-aged and older adults, with the support from adult children serving as a vital pathway for this positive impact. Both economic and emotional assistance from children hold equal importance in this dynamic. It underscores the necessity of fortifying the family support system to amplify the support provided by children, which in turn could significantly enhance the mental health of rural middle-aged and older adults.