Factors affecting ecotourism loyalty with the moderating role of social influence - Empirical evidence in Vietnam

Today, ecotourism has become more common and attractive for tourists who love to explore nature and experience the cultural heritage. In the 4.0 age of technology, social networking not only helps visitors find suitable destinations to visit easily but also provides visitors with a place to leave comments or reviews after the trips. This study aims to qualify the relationships between electronic-worth-of-mouth (eWOM), social influence (SI), destination image (DI), tourist satisfaction (SAT), and ecotourism loyalty (EL). The study applied the PLS-SEM model to estimate 499 observations at ecotourism sites in Vietnam as empirical evidence. The research results show that all the factors in the research model have positive and significant effects on EL. In particular, DI and SAT, directly and indirectly, affect EL; while eWOM and SI only have direct effects on EL. Additionally, it was found that the effect of eWOM on EL increased with the moderating role of SI. © 2022 Editura Universitatii din Oradea. All rights reserved.Univerzita Tomáše Bati ve Zlín

Cerebrospinal fluid lactate concentration to distinguish bacterial from aseptic meningitis: a systemic review and meta-analysis

ABSTRACT: INTRODUCTION: Making a differential diagnosis between bacterial meningitis and aseptic meningitis is a critical clinical problem. The utility of a cerebrospinal fluid (CSF) lactate assay for this purpose has been debated and is not yet routinely clinically performed. To adequately evaluate this assay, a systematic review and meta-analysis of studies of the CSF lactate concentration as a marker for both bacterial meningitis and aseptic meningitis was performed. METHODS: Electronic searches in PubMed, Scopus, the MEDION database and Cochrane Library were conducted to identify relevant articles published before March 2009. A manual search of reference lists from selected articles was also conducted. Two reviewers independently selected relevant articles and extracted data on study characteristics, quality and accuracy. RESULTS: Twenty-five articles were identified that met the eligibility criteria. Diagnostic odds ratios were considerably homogenous (Chi-square p = 0.1009, I2 = 27.6%), and the homogeneity was further confirmed by a Galbraith plot and meta-regression analysis using several covariates. The symmetrical summary receiver-operator characteristic curve (SROC), fitted using the Moses-Shapiro-Littenberg method, was positioned near the upper left corner of the SROC curve. The Q value and area under the curve were 0.9451 and 0.9840, respectively, indicating excellent accuracy. The diagnostic accuracy of the CSF lactate concentration was higher than those of other four conventional markers (CSF glucose, CSF/plasma glucose quotient, CSF protein, and CSF total number of leukocytes) using a head to head meta-analysis of the 25 included studies. CONCLUSIONS: To distinguish bacterial meningitis from aseptic meningitis, CSF lactate is a good single indicator and a better marker compared to other conventional markers

Impact of HPV vaccination and cervical screening on cervical cancer elimination: a comparative modelling analysis in 78 low-income and lower-middle-income countries.

BACKGROUND: The WHO Director-General has issued a call for action to eliminate cervical cancer as a public health problem. To help inform global efforts, we modelled potential human papillomavirus (HPV) vaccination and cervical screening scenarios in low-income and lower-middle-income countries (LMICs) to examine the feasibility and timing of elimination at different thresholds, and to estimate the number of cervical cancer cases averted on the path to elimination. METHODS: The WHO Cervical Cancer Elimination Modelling Consortium (CCEMC), which consists of three independent transmission-dynamic models identified by WHO according to predefined criteria, projected reductions in cervical cancer incidence over time in 78 LMICs for three standardised base-case scenarios: girls-only vaccination; girls-only vaccination and once-lifetime screening; and girls-only vaccination and twice-lifetime screening. Girls were vaccinated at age 9 years (with a catch-up to age 14 years), assuming 90% coverage and 100% lifetime protection against HPV types 16, 18, 31, 33, 45, 52, and 58. Cervical screening involved HPV testing once or twice per lifetime at ages 35 years and 45 years, with uptake increasing from 45% (2023) to 90% (2045 onwards). The elimination thresholds examined were an average age-standardised cervical cancer incidence of four or fewer cases per 100 000 women-years and ten or fewer cases per 100 000 women-years, and an 85% or greater reduction in incidence. Sensitivity analyses were done, varying vaccination and screening strategies and assumptions. We summarised results using the median (range) of model predictions. FINDINGS: Girls-only HPV vaccination was predicted to reduce the median age-standardised cervical cancer incidence in LMICs from 19·8 (range 19·4-19·8) to 2·1 (2·0-2·6) cases per 100 000 women-years over the next century (89·4% [86·2-90·1] reduction), and to avert 61·0 million (60·5-63·0) cases during this period. Adding twice-lifetime screening reduced the incidence to 0·7 (0·6-1·6) cases per 100 000 women-years (96·7% [91·3-96·7] reduction) and averted an extra 12·1 million (9·5-13·7) cases. Girls-only vaccination was predicted to result in elimination in 60% (58-65) of LMICs based on the threshold of four or fewer cases per 100 000 women-years, in 99% (89-100) of LMICs based on the threshold of ten or fewer cases per 100 000 women-years, and in 87% (37-99) of LMICs based on the 85% or greater reduction threshold. When adding twice-lifetime screening, 100% (71-100) of LMICs reached elimination for all three thresholds. In regions in which all countries can achieve cervical cancer elimination with girls-only vaccination, elimination could occur between 2059 and 2102, depending on the threshold and region. Introducing twice-lifetime screening accelerated elimination by 11-31 years. Long-term vaccine protection was required for elimination. INTERPRETATION: Predictions were consistent across our three models and suggest that high HPV vaccination coverage of girls can lead to cervical cancer elimination in most LMICs by the end of the century. Screening with high uptake will expedite reductions and will be necessary to eliminate cervical cancer in countries with the highest burden. FUNDING: WHO, UNDP, UN Population Fund, UNICEF-WHO-World Bank Special Program of Research, Development and Research Training in Human Reproduction, Canadian Institute of Health Research, Fonds de recherche du Québec-Santé, Compute Canada, National Health and Medical Research Council Australia Centre for Research Excellence in Cervical Cancer Control

Unconventional Low-Cost Fabrication and Patterning Techniques for Point of Care Diagnostics

The potential of rapid, quantitative, and sensitive diagnosis has led to many innovative ‘lab on chip’ technologies for point of care diagnostic applications. Because these chips must be designed within strict cost constraints to be widely deployable, recent research in this area has produced extremely novel non-conventional micro- and nano-fabrication innovations. These advances can be leveraged for other biological assays as well, including for custom assay development and academic prototyping. The technologies reviewed here leverage extremely low-cost substrates and easily adoptable ways to pattern both structural and biological materials at high resolution in unprecedented ways. These new approaches offer the promise of more rapid prototyping with less investment in capital equipment as well as greater flexibility in design. Though still in their infancy, these technologies hold potential to improve upon the resolution, sensitivity, flexibility, and cost-savings over more traditional approaches

Employee retention and the moderating role of psychological ownership in retail

Retailing is one of the critical stages in supply chain operations, in which human resources and employee retention play a decisive role as in any organization. Based on motivation theories for employee retention (ER), this study examines the integrated indirect effects of organizational and personal motivators on ER through employee engagement (EE) in the retail industry. Furthermore, it assesses how psychological ownership (PO) directly affects ER and moderates the effect of ER on EE of full-time employees in the Vietnamese context as empirical evidence. The combination of a qualitative methodology (in-depth interviews with retail experts) and a quantitative methodology (a survey conducted with 571 fulltime retail employees) is deployed. PLS-SEM with SmartPLS is utilized for data analysis and hypothesis testing. The study findings demonstrate that the integrated roles of organizational and personal motivators significantly affect ER through EE in retail companies. Interestingly, the study discovered that PO has a significant positive influence on ER, but a higher PO can reduce the relationship between EE and ER. Practically, the study highlights the implication that organizational motivators may not be sufficient to retain employees, since the intention of employees to remain or quit also depends on personal factors. It also suggests that in the working environment with a solid relationship between EE and ER, PO can lead to negative employee behaviour, such as bias, misconduct, and disengagement, which may harm the company.Internal Grant Agency of FaME, Tomas Bata University in Zlin; Southeast Asia regio

Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets

Objective: Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Methods: Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Results: Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Conclusions: Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated

Applying machine learning to identify anti‐vaccination tweets during the covid‐19 pandemic

Anti‐vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti‐vaccination content widely available on social media, including Twitter. Being able to identify anti‐vaccination tweets could provide useful information for formulating strategies to reduce anti‐vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti‐vaccination tweets that were published during the COVID‐19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long shortterm memory networks with pre‐trained GLoVe embeddings (Bi‐LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi‐LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi‐LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti‐vaccination tweets in future studies