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The purpose of this paper is to describe Karen refugee women’s experience of resettlement and the factors which structured community capacity to support their mental health and well-being.

A postcolonial and feminist standpoint was used to bring Karen women’s voice to the knowledge production process. Data were collected through ethnographic field observation, in-depth semi-structured individual and focus group interviews with Karen women as well as healthcare and social service providers.

Three interrelated themes emerged from the data: Karen women’s construction of mental health as “stress and worry”; gender, language and health literacy intersected, shaping Karen women’s access to health care and social resources; flexible partnerships between settlement agencies, primary care and public health promoted community capacity but were challenged by neoliberalism.

Karen women and families are a diverse group with a unique historical context. Not all the findings are applicable across refugee women.

This paper highlights the social determinants of mental health for Karen women and community responses for mitigating psychological distress during resettlement.

Public health policy requires a contextualized understanding of refugee women’s mental health. Health promotion in resettlement must include culturally safe provision of health care to mitigate sources of psychological distress during resettlement.

This research brings a postcolonial and feminist analysis to community capacity as a public health strategy.

Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

Impairments in social cognitions in schizophrenia are increasingly reported in the last decade but only a few studies have come from Asia. The objective of the study was to evaluated emotion perception, theory of mind and social knowledge in people with schizophrenia compared to healthy controls. Participants were 36 clinically stable outpatients with schizophrenia and 36 normal controls with comparable age and level of education. We administered general neurocognition test (the Addenbrooke's Cognitive Examination), emotion perception (the Faces Test), theory of mind (the Eyes Test) and social knowledge (the Situational Features Recognition Test; SFRT). Schizophrenia patients displayed obvious impairment in all three social cognition domains i.e. the Faces Test [13.7 (2.9) vs 15.7 (1.9), P=0.001], the Eyes Test [18.9 (4.4) vs 23.5 (4.4), P<0.001] and SFRT [0.85 (0.09) vs 0.9 (0.05), P=0.002]. The performances on three social cognition tests did not correlate with positive symptoms. Only the Faces Test seemed to be related to negative symptoms. The results demonstrated that there are deficits of social cognitions in schizophrenia even in a clinically stable population.

Financial stability has become a focal point for central banks since the global financial crisis. However, the optimal mix between monetary and financial stability policies remains unclear. In this study, the “soft” approach to such policy mix was tested – how often monetary policy (in inflation reports) analyses financial stability issues. This paper aims to discuss the aforementioned objective.

A total of 648 inflation reports published by 11 central banks from post-communist countries in 1998-2019 were reviewed using a text-mining method.

Results show that financial stability topics (mainly cyclical aspects of systemic risk) on average account for only 2%of inflation reports’ content. Although this share has grown somewhat since the global financial crisis (in CZ, HU and PL), it still remains at a low level. Thus, not enough evidence was found on the use of a “soft” policy mix in post-communist countries.

Given the strong interactions between price and financial stability, this paper emphasizes the need to increase the attention of monetary policymakers to financial stability issues.

The study combines two research areas, i.e. monetary policy and modern text mining techniques on a sample of post-communist countries, something which to the best of the authors’ knowledge has not been sufficiently explored in the literature before.

Since the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.

To explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.

The results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).

To the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

Parental feeding practice (PFP) plays an important role in child’s eating behavior and weight status, but less information is available about its role in the Thai family setting. The purpose of this paper is to examine the influence of PFP on child’s gender and body mass index (BMI).

Participants included 227 parents-child dyads from the suburban area of Nakhon Pathom province, Thailand. Children aged 9-12 years and parents who were either child’s mother, father or grandfather/grandmother were enrolled in the study. Body weight, height, waist circumference and body fat were measured in all children. Eating behavior of each child was assessed by using child’s eating questionnaire. Parents also provided their feeding practices in child feeding questionnaires. Information on household food security was also obtained from children’s parents.

There was significant difference in eating behaviors and home environment between child’s genders. For child’s eating behavior, mean total eating scores of girls were significantly greater (p=0.002) than that of boys and that the inappropriate home environment was more found in families of boys than girls. Regarding feeding practice, parents used more food restriction (p=0.008) and monitoring on child’s eating (p=0.042) in girls than boys. Parents put more pressure to eat on the normal weight than obese children (p=0.001). Regression analysis revealed that, apart from parental BMI and household income, PFPs have a significant impact (15.6 percent explained variance) on child’s BMI.

This study highlights the importance of being aware of child’s gender and weight status when feeding practices were provided to them. Nutrition education for parents should take account for parents’ perceptions and concerns as well as the modification of feeding practices to improve children’s eating behaviors.