OBJECTIVE: Parents of children diagnosed with severe-to-profound sensorineural hearing loss may experience a range of emotions owing to a lack of knowledge and experience in dealing with such children. However, most audiology clinics only attend to children with deaf and hard of hearing (DHH) and not their parents. Thus, parents\' emotional and support needs are frequently excluded from the intervention sessions, making their own needs invisible. This study aimed to identify academic and clinical instruments used for assessing parental emotional status (PES) and ecological support systems (ESS) in early intervention and determine the factors affecting PES and ESS among parents of DHH children undergoing cochlear implantation.
METHODS: This scoping review followed the rigorous methodological framework; searched Medline (via OVID and EMBSCO), Scopus, and Web of Science; and selected studies relevant to validated instruments used to evaluate the PES and ESS among parents of DHH children below 6 years old. Before selecting and reviewing relevant articles, two reviewers independently assessed article titles and abstracts from the data sources. Two reviewers verified half of the first reviewer\'s extracted data.
RESULTS: Overall, 3060 articles were retrieved from the database search, and 139 were selected for full-text review following title and abstract reviews. Ultimately, this study included 22 articles. Among them, 23 and 12 validated instruments, most of which are generic measures, were used for assessing PES and ESS, respectively. Three condition-specific instruments were identified and designed to be administered following cochlear implantation surgery.
CONCLUSIONS: This study revealed that healthcare professionals who interact with parents of DHH children lack the necessary instruments, particularly for parents of children undergoing cochlear implantation surgery. Therefore, it is necessary to develop condition-specific instruments for parents who consider cochlear implantation for their children.

Background: Sleep and affective states are closely intertwined. Nevertheless, previous methods to evaluate sleep-affect associations have been limited by poor ecological validity, with a few studies examining temporal or dynamic interactions in naturalistic settings. Objectives: First, to update and integrate evidence from studies investigating the reciprocal relationship between daily sleep and affective phenomena (mood, affect, and emotions) through ambulatory and prospective monitoring. Second, to evaluate differential patterns based on age, affective disorder diagnosis (bipolar, depression, and anxiety), and shift work patterns on day-to-day sleep-emotion dyads. Third, to summarise the use of wearables, actigraphy, and digital tools in assessing longitudinal sleep-affect associations. Method: A comprehensive PRISMA-compliant systematic review was conducted through the EMBASE, Ovid MEDLINE(R), PsycINFO, and Scopus databases. Results: Of the 3024 records screened, 121 studies were included. Bidirectionality of sleep-affect associations was found (in general) across affective disorders (bipolar, depression, and anxiety), shift workers, and healthy participants representing a range of age groups. However, findings were influenced by the sleep indices and affective dimensions operationalised, sampling resolution, time of day effects, and diagnostic status. Conclusions: Sleep disturbances, especially poorer sleep quality and truncated sleep duration, were consistently found to influence positive and negative affective experiences. Sleep was more often a stronger predictor of subsequent daytime affect than vice versa. The strength and magnitude of sleep-affect associations were more robust for subjective (self-reported) sleep parameters compared to objective (actigraphic) sleep parameters.

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.

Negative and positive urgency are two closely related personality traits that reflect the tendency for an individual to engage in maladaptive risk-taking in response to extreme negative and positive emotions, respectively. However, other prominent emotion theories describe how emotions contribute to adaptive, rather than maladaptive, decision-making. This conceptual review considers how Urgency Theory can be integrated with these broader existing emotion theories. We proceed as follows: a) briefly define what is meant by emotions in science and summarize basic human neuroscience underlying emotions; b) briefly describe select theories and research linking emotions to adaptive decision-making, including brain correlates of this effect; c) review Urgency Theory, including contrasting evidence that emotions lead to maladaptive outcomes and brain correlates of this effect; d) discuss how urgency can be integrated into theories that view emotions as both adaptive and maladaptive for decision-making; and e) propose future directions to advance research in this field. We identified four, not mutually exclusive, viable options to integrate Urgency Theory into existing theories: urgency as model-free emotion regulation, urgency as being driven by incidental emotions, urgency as a reflexive response to emotions, or urgency as an individual difference factor. We conclude that although all four options are viable, individual difference and model-free emotion regulation have the most empirical support to date. Importantly, the other two options are less well-researched. Direct tests comparing these integrations is necessary to determine the most accurate way to integrate urgency with existing emotion theories. We believe that this research can identify mechanisms underlying urgency and help inform future intervention and prevention development to reduce negative effects of urgency across numerous maladaptive behaviors and clinical disorders.

As the smallest spatial unit in the city, the community should be an important space for healing the urban population. This paper presents a systematic review summarizing studies on community healing environments. The study identified 75 publications. The research findings are as follows: the main intervening variables include restorativeness, emotions and mood, stress, and well-being. Little attention has been paid to the intervening variables including social support, environmental preferences and satisfaction, place attachment, behavior types that respond to personal will, self-fulfillment, and the spirit of personal culture. The various types of natural environments in communities have been given focused attention for a long time. However, the superiority of the natural environment should not be a reason to underestimate the healing benefits of community spaces. The mechanisms by which environmental characteristics are transformed into positive individual influences through intervening variables should be explored.

UNASSIGNED: Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field.
UNASSIGNED: This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings.
UNASSIGNED: The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments.
UNASSIGNED: The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students\' emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems.
UNASSIGNED: This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.

Narrative identity allows individuals to integrate their personal experiences into a coherent and meaningful life story. Addictive disorders appear to be associated with a disturbed sense of self, reflected in problematic and disorganized self-narratives. In recent literature, a growing body of research has highlighted how narrative approaches can make a dual contribution to the understanding of addiction: on the one hand, by revealing crucial aspects of self structure, and, on the other, by supporting the idea that addiction is a disorder related to unintegrated self-states in which dissociative phenomena and the resulting sense of \'loss of self\' are maladaptive strategies for coping with distress. This conceptual review identified the main measures of narrative identity, i.e., narrative coherence and complexity, agency, and emotions, and critically examines 9 quantitative and qualitative studies (out of 18 identified in literature), that have investigated the narrative dimension in people with an addictive disorder in order to provide a synthesis of the relationship between self, narrative and addiction. These studies revealed a difficulty in the organization of narrative identity of people with an addictive disorder, which is reflected in less coherent and less complex autobiographical narratives, in a prevalence of passivity and negative emotions, and in a widespread presence of themes related to a lack of self-efficacy. This review points out important conceptual, methodological and clinical implications encouraging further investigation of narrative dimension in addiction.

OBJECTIVE: The aim of this review is to provide an overview of parental communication patterns during mealtimes, with a special emphasis being placed on the differences between families with and without a history of eating disorders.
METHODS: The systematic review was conducted according to the PRISMA statement. A systematic literature search was carried out in PubMed, PubPsych and PsycINFO and the results were assessed for eligibility by two independent raters using the PICOS criteria. Only studies that included a mealtime observation were considered suitable for analysis of both explicit and implicit parental communication.
RESULTS: The results of the review suggest that mothers communicate more, with more complexity, and with a greater variety of words with their children during mealtimes compared to fathers. The intention and type of communication is diverse and heterogeneous. In general, parents often tried to encourage their children to eat. Verbal modeling and co-eating appeared to be common behaviors. Mothers with a history of eating disorders expressed more negative emotions during eating than mothers without eating disorders. Findings regarding the use of positive comments and controlling speech are contradicting.
CONCLUSIONS: The review outlines major fields of parent-child communication and modeling behavior around family meals which might be relevant to investigate and integrate into models of intergenerational transmission of eating behavior and disordered eating.

BACKGROUND: Evaluation of post-nephrectomy social health in living kidney donors is essential. This systematic review examines their emotional need for social relatedness post-donation.
METHODS: Following the PRISMA guidelines, we systematically searched Scopus, CINAHL, and PsycINFO.
RESULTS: Among the screened records, 32 quantitative and 16 qualitative papers met the inclusion criteria. Quantitative research predominantly utilized questionnaires featuring generic items on social functioning. However, a minority delved into emotional and social dimensions, aligning with qualitative studies emphasizing the importance of social connection and perceived social support post-donation. Specifically, post-donation changes in connecting with others encompass a sense of belongingness, heightened autonomy, shifts in concern for the recipient\'s health, and continued care by shielding the recipient from personal health issues. Social acknowledgment and social support from both close and extended networks are reported as relevant for recovery after nephrectomy.
CONCLUSIONS: These findings underscore the necessity for targeted measures of emotional needs and social functioning to effectively assess post-donation adjustment. They also inform the identification of key health themes for kidney donor Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) questions.

Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human-computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and \"Other NNs\", which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends.