The purpose of this study is to systematically explore lifestyle hotel guests\' aesthetic experiences. This study adopts word frequency analysis, latent Dirichlet allocation (LDA) topic modelling analysis and manual coding to systematically analyse 11,239 online reviews posted by guests from 131 lifestyle hotels in eight cities in China. A framework is developed to organize the identified themes and illustrate lifestyle hotel guests\' aesthetic experiences. The framework revealed that lifestyle hotels embrace the concept of \"bleisure\" travel-blending business and leisure by offering high-end lodging, flexible tourism destination elements, and event services that cater to the needs of today\'s independent guests. The findings suggest that lifestyle hotel guests stress multiple functions of a hotel, especially the spiritual. Guided by the aesthetic experience at lifestyle hotels, hotel managers can cater to the full spectrum of hotel guests\' aesthetic experience when implementing marketing strategies.

Databases have become important tools in improving health care. Care Response is a database containing information on tens of thousands of chiropractic patients internationally. It has been collecting patient-reported outcomes and patient satisfaction information for more than 10 years. The purpose of this study was to contribute to the understanding of patient perceptions and priorities for chiropractic care by analysing free text entered into the patient reported experience measure (PREM) questionnaires within the Care Response system.
There were two questions of interest on the PREM for this study. One requested information about \"good points\" patients perceived about patients\' care experience, and the other requested information on \"improvements\" that could make the experience better. We conducted a word frequency analysis using a word counting macro in Microsoft Word, then used those results as a starting point for a qualitative analysis. Data were collected on 30 May 2022.
The people who participated in the Care Response system often reported positive experiences with their chiropractors, including that they had reduced pain, improved function, and felt validated in their clinical condition. In addition, they appreciated having diagnostic and treatment procedures explained to them. They valued friendly, professional, and on-time service. The negative experiences were the opposite: being rushed through treatment, that the treatment was not worth the cost, or that they weren\'t treated professionally, empathetically, or with respect for them as individuals. The most important themes that emerged under \"good points\" were satisfaction (with care), value (as a person), safety, comfort, and professionalism. Their opposites, dissatisfaction, lack of value, lack of safety, lack of comfort, and lack of professionalism emerged as the most important themes under \"improvements\". We report some nuances of patient experience that have not previously been explored in the literature.
Respondents seemed to value effective care provided in a safe, professional, friendly, and aesthetically pleasing environment. Chiropractors should note these priorities and engage with patients according to them. Education institutions should consider how good practice in these areas might be incorporated into curricula.

The COVID-19 infodemic, characterized by the rapid spread of misinformation and unverified claims related to the pandemic, presents a significant challenge. This paper presents a comparative analysis of the COVID-19 infodemic in the English and Chinese languages, utilizing textual data extracted from social media platforms. To ensure a balanced representation, two infodemic datasets were created by augmenting previously collected social media textual data. Through word frequency analysis, the 30 most frequently occurring infodemic words are identified, shedding light on prevalent discussions surrounding the infodemic. Moreover, topic clustering analysis uncovers thematic structures and provides a deeper understanding of primary topics within each language context. Additionally, sentiment analysis enables comprehension of the emotional tone associated with COVID-19 information on social media platforms in English and Chinese. This research contributes to a better understanding of the COVID-19 infodemic phenomenon and can guide the development of strategies to combat misinformation during public health crises across different languages.

There is an absent systematic analysis or review that has been conducted to clarify the topic of nomenclature history and terms misuse about Chiari malformations (CMs). We reviewed all reports on terms coined for CMs for rational use and provided their etymology and future development. All literature on the nomenclature of CMs was retrieved and extracted into core terms. Subsequently, keyword analysis, preceding and predicting (2023-2025) compound annual growth rate (CAGR) of each core term, was calculated using a mathematical formula and autoregressive integrated moving average model in Python. Totally 64,527 CM term usage was identified. Of these, 57 original terms were collected and then extracted into 24 core-terms. Seventeen terms have their own featured author keywords, while seven terms are homologous. The preceding CAGR of 24 terms showed significant growth in use for 18 terms, while 13, three, three, and five terms may show sustained growth, remain stable, decline, and rare in usage, respectively, in the future. Previously, owing to intricate nomenclature, Chiari terms were frequently misused, and numerous seemingly novel but worthless even improper terms have emerged. For a very basic neuropathological phenomenon tonsillar herniation by multiple etiology, a mechanism-based nosology seems to be more conducive to future communication than an umbrella eponym. However, a good nomenclature also should encapsulate all characteristics of this condition, but this is lacking in current CM research, as the pathophysiological mechanisms are not elucidated for the majority of CMs.

The first mass migration in China took place at the end of the Western Jin, which resulted in the southward transfer of the Central Plains Culture and brought about huge social changes. Such social changes exerted significant impacts on the gentry of the Jin Dynasty. This paper used a huge volume of Classical Chinese legacy text of Jin gentry members. We used CC-LIWC to calculate frequencies of different word categories used in these text contents and conducted an analysis of variance to measure significant differences between the three groups. We found 16 categories of words with significant differences and calculated their effect sizes, such as tense markers (tensem), F = 3.588, P < 0.05, η2 = 0.034; modal particles (modal_pa), F = 3.468, P < 0.05, η2 = 0.053; words for affective processes (affect), F = 3.096, P < 0.05, η2 = 0.028; words for cognitive processes (cogproc), F = 3.308, P < 0.05, η2 = 0.031; words for perceptual processes (percept), F = 7.137, P < 0.05, η2 = 0.06. Combining the psycholinguistics of the 16 categories of words and researches of historians on the Jin Dynasty, we then analyzed the direct and indirect, immediate and long-lasting psycholinguistic impacts of this mass migration on the gentry themselves and their descendants.

Doctor-patient relationships (DPRs) in China have been straining. With the emergence of the COVID-19 pandemic, the relationships and interactions between patients and doctors are changing. This study investigated how patients\' attitudes toward physicians changed during the pandemic and what factors were associated with these changes, leading to insights for improving management in the healthcare sector. This paper collected 58,600 comments regarding Chinese doctors from three regions from the online health platform Good Doctors Online (haodf.com, accessed on 13 October 2022). These comments were analyzed using text mining techniques, such as sentiment and word frequency analyses. The results showed improvements in DPRs after the pandemic, and the degree of improvement was related to the extent to which a location was affected. The findings also suggest that administrative services in the healthcare sector need further improvement. Based on these results, we summarize relevant recommendations at the end of this paper.

Mental disorders are recognized as leading causes of morbidity and disability worldwide, but the etiology of mental disorders is not completely clear, and it is generally believed that genetic and environmental factors are involved. A number of brain imaging researches showed that patients with mental disorders often had multiple brain structural abnormalities. Which brain structures are dominant in the pathogenesis of mental disorders, and whether there is a regular relationship between them need to be further studied. In this study, we used text mining technology to analyze the literatures related to mental disorders and brain structures in Pubmed database. Firstly, 61 high-frequency brain structures identified as the major brain structures. Then, from the results of system clustering, the major brain structures were divided into three clusters. Finally, 29 frequent itemsets and 36 strong association rules were obtained by association analysis. This study applied text mining technology to summarize and clarify the relationship between mental disorders and brain structures, providing possible direction and reference for future experimental studies.

COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people\'s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.