Self-esteem and professional identity among male nurses and male nursing students: mediating roles of perceived prejudice and psychological distress

IntroductionThere are not enough nurses around the world, and there are even fewer male nurses. It has not been easy for men to become nurses because of stereotypes about the roles of men and women in the workplace, which lead to prejudice and discrimination. This study explored how the self-esteem of male nurses and male nursing students affects their professional identity in an environment where stereotypes and social prejudice exist. This study also examined the differences of relevant variables in different sociodemographic characteristics of the research subjects in a Chinese social context.MethodsBy purposive and snowball sampling, 464 male nurses and male nursing students were surveyed through questionnaires from November 2021 to January 2022. Data analysis was performed using SPSS 25.0 and PROCESS Macro 3.3.ResultsSelf-esteem could indirectly affect professional identity through perceived prejudice and psychological distress. Nonetheless, self-esteem still had a significant direct effect on professional identity. The total mediating effect accounted for 32.816% of the total effect, and the direct effect accounted for 67.184% of the total effect. Also of note was that 81.7% of participants reported experiencing psychological distress.DiscussionTo improve the professional identity of male nurses and male nursing students, nursing educators and administrators should do the following: protect and improve their self-esteem; take steps to reduce social prejudice against them; value their mental health and alleviate their psychological distress

POM-A Mobile Agent Security Model against Malicious Hosts

Security, especially the attacks performed by hosts to the visiting mobile agents (the malicious hosts problem), is a major obstacle that prevents mobile agent technology from being widely adopted. Being the running environment for mobile agents, the host has full control over them and could easily perform many kinds of attacks against them. This problem has not been full solved yet. After the analysis of mobile agent security requirements, this paper first gives some design issues of a practical security model of malicious hosts problem. Based on this, POM (Police Office Model), a mobile agent security model, is presented. By setting up special hosts called police offices within regions, POM can prevent most attacks performed by malicious hosts through the separation of the safetycritical parts of mobile agents from the malicious hosts. 1

CLOPE: A fast and effective clustering algorithm for transactional data

This paper studies the problem of categorical data clustering, especially for transactional data characterized by high dimensionality and large volume. Starting from a heuristic method of increasing the height-to-width ratio of the cluster histogram, we develop a novel algorithm- CLOPE, which is very fast and scalable, while being quite effective. We demonstrate the performance of our algorithm on two real world datasets, and compare CLOPE with the state-of-art algorithms