Attitudes, acceptance and hesitancy among the general population worldwide to receive the COVID-19 vaccines and their contributing factors: A systematic review.

COVID-19 vaccines have met varying levels of acceptance and hesitancy in different parts of the world, which has implications for eliminating the COVID-19 pandemic. The aim of this systematic review is to examine how and why the rates of COVID-19 vaccine acceptance and hesitancy differ across countries and continents. PubMed, Web of Science, IEEE Xplore and Science Direct were searched between 1 January 2020 and 31 July 2021 using keywords such as “COVID-19 vaccine acceptance”. 81 peer-reviewed publications were found to be eligible for review. The analysis shows that there are global variations in vaccine acceptance among different populations. The vaccine-acceptance rates were the highest amongst adults in Ecuador (97%), Malaysia (94.3%) and Indonesia (93.3%) and the lowest amongst adults in Lebanon (21.0%). The general healthcare workers (HCWs) in China (86.20%) and nurses in Italy (91.50%) had the highest acceptance rates, whereas HCWs in the Democratic Republic of Congo had the lowest acceptance (27.70%). A nonparametric one-way ANOVA showed that the differences in vaccine-acceptance rates were statistically significant (H (49) = 75.302, p = 0.009*) between the analyzed countries. However, the reasons behind vaccine hesitancy and acceptance were similar across the board. Low vaccine acceptance was associated with low levels of education and awareness, and inefficient government efforts and initiatives. Furthermore, poor influenza-vaccination history, as well as conspiracy theories relating to infertility and misinformation about the COVID-19 vaccine on social media also resulted in vaccine hesitancy. Strategies to address these concerns may increase global COVID-19 vaccine acceptance and accelerate our efforts to eliminate this pandemic.

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Global COVID-19 Vaccine Acceptance: A Systematic Review of Associated Social and Behavioral Factors

Abstract Necessary condition analysis (NCA) is an increasingly used or suggested method in many business and management disciplines including, for example, entrepreneurship, human resource management, international business, marketing, operations, public and nonprofit management, strategic management, and tourism. In the light of this development, our work delivers a review of the topics analyzed with NCA or in which NCA is proposed as a method. The review highlights the tremendous possibilities of using NCA, which hopefully encourages other researchers to try the method. To support researchers in future NCA studies, this article also provides detailed guidelines about how to best use NCA. These cover eight topics: theoretical justification, meaningful data, scatter plot, ceiling line, effect size, statistical test, bottleneck analysis, and further descriptions of NCA. 

/pdf/necessary-condition-analysis-nca-review-of-research-topics-2qdkdp5x.pdf

Necessary condition analysis (NCA): review of research topics and guidelines for good practice

SARS-CoV-2 (COVID-19) vaccines are critical for containing serious infections. However, as COVID-19 evolves toward more transmissible varieties and serum antibody levels in vaccinated persons steadily decline over time, the likelihood of breakthrough infections increases. This is a cross-sectional study based on an online questionnaire for Jordanian adults (n = 915) to determine how individuals who have finished the current vaccination regimen feel about a prospective COVID-19 booster shot and what factors might influence their decision. Almost half of the participants (44.6%) intended to get the booster dose of the COVID-19 vaccine. The most frequently mentioned reasons for participants' reluctance to get the COVID-19 vaccine booster dose were "The benefits of booster dose have not been scientifically proven" (39.8%), followed by "I took the last dose a short time ago, and there will be no need to take the booster dose for at least a year" (24.6%). In turn, "I was infected with COVID-19; thus, I do not require the booster dose" was the least reported reason (13.1%). These findings highlight the considerable hesitancy toward COVID-19 booster immunization among Jordanians, as well as the variables associated with vaccine hesitancy and the most frequently mentioned reasons for hesitancy, which will aid in creating excellent campaigns regarding booster doses. 

/pdf/willingness-of-the-jordanian-population-to-receive-a-covid-1lozxyk5.pdf

Willingness of the Jordanian Population to Receive a COVID-19 Booster Dose: A Cross-Sectional Study

Introduction Vaccination is an essential intervention to curb the coronavirus disease 2019 (COVID-19) pandemic. This review aimed to estimate the pooled proportion of COVID-19 vaccine acceptance worldwide. Methods A systematic search of the MEDLINE (PubMed) database using “COVID-19,” “vaccine” and “acceptance” to obtain original research articles published between 2020 and July 2021. Only studies with full text and that were published in English were included. The Joanna Briggs Institute meta-analysis was used to assess the data quality. The meta-analysis was performed using generic inverse variance with a random-effects model using the Review Manager software. Results A total of 172 studies across 50 countries worldwide were included. Subgroup analyses were performed with regard to vaccine acceptance, regions, population, gender, vaccine effectiveness, and survey time. The pooled proportion of COVID-19 vaccine acceptance was 61% (95% CI: 59, 64). It was higher in Southeast Asia, among healthcare workers, in males, for vaccines with 95% effectiveness, and during the first survey. Conclusion COVID-19 vaccine acceptance needs to be increased to achieve herd immunity to protect the population from the disease. It is crucial to enhance public awareness of COVID-19 vaccination and improve access to vaccines. Systematic Review Registration PROSPERO 2021, identifier CRD42021268645.

Systematic Review and Meta-Analysis of COVID-19 Vaccination Acceptance

The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields. Despite its growing success, there are some limitations and disadvantages: the most significant is the lack of interpretability that does not allow users to understand how particular decisions are made. Our study focus on one of the best performing and most used models in the Machine Learning framework, the Random Forest model. It is known as an efficient model of ensemble learning, as it ensures high predictive precision, flexibility, and immediacy; it is recognized as an intuitive and understandable approach to the construction process, but it is also considered a Black Box model due to the large number of deep decision trees produced within it. The aim of this research is twofold. We present a survey about interpretative proposal for Random Forest and then we perform a machine learning experiment providing a comparison between two methodologies, inTrees, and NodeHarvest, that represent the main approaches in the rule extraction framework. The proposed experiment compares methods performance on six real datasets covering different data characteristics: n. of observations, balanced/unbalanced response, the presence of categorical and numerical predictors. This study contributes to picture a review of the methods and tools proposed for ensemble tree interpretation, and identify, in the class of rule extraction approaches, the best proposal.

A comparison among interpretative proposals for Random Forests

Motivations behind users’ participation in the standard-setting process: Focus on financial analysts

This research establishes the theoretical link between the development of tourism and citizens' trust. The research is grounded in political economy of state intervention in tourism and draws from social exchange theory to build the theoretical model. The latter incorporates variables such as trust, power, knowledge, and benefits and costs of tourism, which are central to any exchange process between social actors. The model distinguishes and proposes a theoretical relationship between domain specific political trust and generic political trust. The former refers to citizens' trust in local government in the specific context of tourism development while the latter refers to citizens' general level of trust in local government. The model is tested using data collected from residents of the metropolitan area of Naples, Italy. Results suggest that residents’ trust in local government in the specific context of tourism strongly influences their general level of trust, suggesting a spill-over effect of political trust. We demonstrated empirically that political trust in the context of tourism and the general trust in an institution are theoretically distinct concepts. The constructs we used to conceptualize tourism development has distinct influence on the two dimensions of political trust.

Validating a Theoretical Model of Citizens’ Trust in Tourism Development

Regional competitiveness is a concept whose definition and applicability is highly debated among scholars. Nevertheless, over recent years it has become widespread among policy makers and practitioners, especially in the European Union. In line with this diffusion a number of alternative composite indicators of regional competitiveness have been released. Instead of building a composite index, this paper carries out empirical analyses whose aim is to analyze the European NUTS2 regions’ performance for those indicators that, according to the literature, contribute to the definition of competitiveness. Results highlight the existence of a competitiveness divide between Northern European countries, which report better performances for most of the competitiveness indicators, and Southern/Eastern ones. The group of more competitive regions expands over time by including regions from Centre European countries while within peripheral regions a stable gap between regions where metropolitan areas are located and the others exists.

Similarities and Differences in Competitiveness Among European NUTS2 Regions: An Empirical Analysis Based on 2010–2013 Data

Measuring retailers’ perceptions of new metro stations inauguration

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