Variables that predict nurses' job stress and intention to leave during the COVID-19 pandemic in Turkey.

AIM: To investigate the variables predicting job stress and intention to leave the job among nurses during the COVID-19 pandemic in Turkey. DESIGN: This research was designed as an analytical cross-sectional study. METHODS: The population of the study consisted of 450 nurses working in a training and research hospital (N = 450). The study sample consisted of 178 nurses who worked in that hospital between February and July 2021. The Nurse Information Form, Job Stress Scale, and Intention to Leave Scale were used as data collection tools. The independent-sample t-test, one-way analysis of variance, and Pearson correlation analysis were applied for univariate analysis while multiple regression analysis was used for multivariate analysis. RESULTS: It was determined that the main factors predicting nurses' job stress levels were their Intention to Leave Scale scores, level of organizational satisfaction. The main factors predicting intention to leave were their Job Stress Scale scores, working conditions, and job satisfaction. A positive moderate correlation was found between job stress and intention to leave. CONCLUSION: This study demostares that among nurses during the COVID-19 pandemic, the predictors of job stress were intention to leave and organizational satisfaction, while the predictors of turnover intention included job stress, working conditions, and job satisfaction. Hospital managers, especially nursing services managers, should develop strategies for extraordinary situations such as pandemics and evaluate the professional satisfaction of nurses while providing a healthy working environment. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contributions.

The effect of pre- and post COVID-19 hazard and risk management of nurses on performance evaluation.

BACKGROUND: It is important to identify the hazards encountered by nurses before and after COVID-19, assess the potential risks and risk levels, take measures for the identified hazards and risks, and determine the effect of risk management on employee performance evaluation. OBJECTIVE: The aim of this study was to determine the effect of hazard and risk management of nurses before and after COVID-19 on performance evaluation. METHODS: The study consisted of two stages. The first stage had a descriptive design, while the second stage had a one-group pre-test and post-test design. The second stage of the study was conducted during the COVID-19 pandemic. There were 377 nurses who agreed to participate in the study in the first stage and 201 nurses in the second stage. The study data was collected via Information Form, Hazard and Risk Identification Form, and Employee Performance Scale (EPS). In the second stage of the study, training was provided using instruction cards called "Means of Protection Against Hazards and Risks" for potential moderate- and high-level hazards and risks existing after the pre-test. The post-test stage was conducted two weeks after the training. Mann-Whitney U Test, Kruskal-Wallis Test, and Spearman's correlation coefficients were used for statistical analyses of descriptive statistics. RESULTS: The mean EPS score was found to be pre training 3,85±0,60, after training was found to be 3.92±0.55. Moreover, there was a weak negative correlation between pre- and post-training risk levels during the COVID-19 pandemic. CONCLUSION: Hazard and risk management training had a positive effect on the performance of employees.

Can artificial intelligence and robotic nurses replace operating room nurses? The quasi-experimental research.

Operating room nurses, who have an important place in the professional healthcare team, should be able to use technology effectively and adapt to innovations. This study is expected to shed light on how effective the development of robotic technologies and artificial intelligence and their integration into operating room nursing will be in fulfilling the requirements of contemporary nursing philosophy. This study was designed as a single group pre- and post-test quasi-experimental study. The quasi-experimental (pretest-posttest) research design was utilized to conduct the study in a Training and Research Hospital in Western Turkey. The nurses (n = 35) working in the operating room of the aforementioned hospital were included in the study. In this study, we aimed to determine whether operating room nurses experienced anxiety due to the use of artificial intelligence and robotic nurses, and the effectiveness of the training given to them in order to raise their awareness. The following three tools were used for data collection: The Nurses' Descriptive Characteristics Form, Artificial Intelligence Knowledge Questionnaire, and Artificial Intelligence Anxiety Scale. Data extraction and analysis were performed in a narrative and tabular way. According to this study, the training given to the operating room nurses significantly increased their knowledge levels about artificial intelligence and robotic nurses, and increased their artificial intelligence- and robotic nurse-related anxiety significantly (p < 0.05). The participating operating room nurses experienced limitations regarding current information, training programs and learning opportunities on robotic surgery. We recommend that the operating room nurses should be provided with trainings on artificial intelligence technologies and robotic nurses, and that they should be enabled to use these information technologies regarding future technologies actively.