درخت حوزه‌های تخصصی

While categorization of human beings into various subgroups in terms of their race, ethnicity, gender, religion, nationality and alike is not a new phenomenon but such a diversity in communities all around the world has drawn much attention to the issue in recent years due to the increasing importance of equality for governments and societies. Workplaces are among those contexts which are proposed to equality issues and are trying to deal with it utilizing different approaches among which creating spirituality in workplace is a noteworthy one. Tourism businesses are not an exception in this regard and are faced with the diversity challenge especially in terms of interaction between different cultures throughout the industry. Additionally, it seems that organizational outcome is influenced by level of diversity at the workplace. Therefore, this paper adopts a developmental descriptive-analytical approach implemented by documentary analysis to discuss the relationship between diversity management and organizational outcome and provides a theoretical model indicating how workplace spirituality (WPS) mediates the relationship between cultural diversity management and organizational outcome in tourism businesses since cultural diversity is as necessary for humankind as biodiversity is for nature and can be a source of competitive advantage for businesses as well.

Nowadays, researchers and managers believe that paying attention to employees' thoughts facilitates sustainable competitive advantage in organizations. After the introduction of cognitive science into organizations, the identification of what it is, how it is formed, and the formulation of Counterfactual Thoughts (CTs) have been investigated and analyzed in the field of Human Resources (HR). In this study, a systematic review of the literature in the field of CT has been done with the aim of identifying the antecedents and key consequences of this field and analyzing them. In this research, a two-stage methodology has been considered including (i) Extraction of antecedents and consequences, (ii) Weighting of antecedents. In the first stage, using the screening of articles based on the seven-step method of Sandelowski and Barroso, the antecedents and consequences of CTs, synthesis and interpretation have also been implemented through the coding method of qualitative interpretive meta-analysis. In the second stage, the consequences extracted in the field of counterfactuals are weighted using the matrix of paired comparisons based on the opinions of experts in the field of HR and AHP, which can be used to evaluate employees' thoughts and behavior. Based on the screening of the articles, the antecedents are categorized into 4 general concepts with 12 codes, and the consequences are classified into 2 and 8 codes. The results of the AHP analysis show that the normalization of the condition related to the behavioral outcome has the greatest impact on the evaluation of employees' thoughts and behavior, while the regulation of related emotions as a result of attitude has the least impact among the codes introduced in this evaluation.

Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of 82.74%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of 83.98%. The proposed model achieved an accuracy of 90.54%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved 94% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.