Background: The emergence of tigecycline-resistant Acinetobacter baumannii has been reported, and the need for tigecycline susceptibility testing in this strain is increasing. However, neither the Clinical & Laboratory Standards Institute, nor the European Commission on Antimicrobial Susceptibility Testing have provided definitive criteria for tigecycline susceptibility testing of A. baumannii. In this study, the disk diffusion method and the minimal inhibitory concentration (MIC) method were com pared to verify conventionally used Food and Drug Administration-identified interpretive criteria to detect tigecycline resistance of A. baumannii. Methods: Forty-four strains of A. baumannii with tigecycline resistance were collected through the Kor-GLASS (Korean Global Antimicrobial Resistance Surveillance System) study in 2022 using the disk diffusion test (DDT). This strain was retested with the MIC method using a Sensititre Gram Negative GN6F AST plate (Thermo Fisher Scientific, USA) to confirm tigecycline resistance. The confirmed strain was subjected to whole genome analysis to elucidate the tigecycline resistance mechanism. Results: Only one of the 44 isolates identified as resistant to tigecycline by the DDT showed resistance with the MIC method, thus the concordance rate of the two methods was 2.3% (1/44). Sequence type 195 strain, carrying bla OXA23 was identified. This strain had no resistance genes of the tetracycline family but had resistance genes to other antimicrobial families. Conclusions Discrepancy of the tigecycline susceptibility test of A. baumannii was identified. To detect tigecycline resistance of A. baumannii, more reliable methods are required.

Background: The emergence of tigecycline-resistant Acinetobacter baumannii has been reported, and the need for tigecycline susceptibility testing in this strain is increasing. However, neither the Clinical & Laboratory Standards Institute, nor the European Commission on Antimicrobial Susceptibility Testing have provided definitive criteria for tigecycline susceptibility testing of A. baumannii. In this study, the disk diffusion method and the minimal inhibitory concentration (MIC) method were com pared to verify conventionally used Food and Drug Administration-identified interpretive criteria to detect tigecycline resistance of A. baumannii. Methods: Forty-four strains of A. baumannii with tigecycline resistance were collected through the Kor-GLASS (Korean Global Antimicrobial Resistance Surveillance System) study in 2022 using the disk diffusion test (DDT). This strain was retested with the MIC method using a Sensititre Gram Negative GN6F AST plate (Thermo Fisher Scientific, USA) to confirm tigecycline resistance. The confirmed strain was subjected to whole genome analysis to elucidate the tigecycline resistance mechanism. Results: Only one of the 44 isolates identified as resistant to tigecycline by the DDT showed resistance with the MIC method, thus the concordance rate of the two methods was 2.3% (1/44). Sequence type 195 strain, carrying bla OXA23 was identified. This strain had no resistance genes of the tetracycline family but had resistance genes to other antimicrobial families. Conclusions Discrepancy of the tigecycline susceptibility test of A. baumannii was identified. To detect tigecycline resistance of A. baumannii, more reliable methods are required.

Purpose: This study aimed to determine the impact of work environment, missed nursing care, and non-nursing tasks on the job stress of hospital nurses. Methods: A descriptive, cross-sectional survey of 164 nurses working in three hospitals was conducted. Data were collected using structured questionnaires and analyzed using descriptive statistics, t-test, one-way analysis of variance, Pearson’s correlation coefficient, and multiple regression analysis using SPSS/WIN 26.0. Results: Hospital Nurses' job stress was negatively correlated with the work environment (r=-.37, p<.001). Job stress had a statistically significant positive correlation with non-nursing tasks (r=.34, p<.001);however, it had no significant correlation with missed nursing care (r=.05, p=.552). Regression model analysis revealed that the variables associated with job stress were non-nursing tasks (β=.29, p<.001) and the foundation for quality nursing care (β=-.25, p=.034), which are subdomains of the work environment. In contrast, missed nursing care was not determined to be a significant factor. Conclusion To effectively manage nurses' job stress, nurse managers should provide higher quality care by reducing the number of non-nursing tasks and creating a positive work environment, especially by providing adequate nursing workforce and material support.

Purpose: This study aimed to explore the influence of teamwork and grit on job adaptation and intention to remain among advanced beginner nurses. Methods: A cross-sectional descriptive research design employing a survey method was used. The study involved 203 advanced beginner nurses with 13~36 months of experience working at a tertiary hospital. Collected data were analyzed using descriptive statistics, independent t-tests, one-way ANOVA, Pearson's correlation coefficients, and hierarchical regression analysis. Results: Job adaptation positively correlated with teamwork (r=.58, p<.001) and grit (r=.65, p<.001). Intention to remain was also positively correlated with teamwork (r=.32, p<.001) and grit (r=.59, p<.001). A hierarchical multiple regression model including control variables, teamwork, and grit accounted for 64% of the factors influencing job adaptation. Another hierarchical multiple regression model, incorporating control variables, teamwork, and grit, explained 44% of the factors influencing the intention to remain. Among the related factors, grit emerged as the most influential in regarding job adaption and intention to remain. Conclusion These findings underscore the importance of establishing a support system to bolster grit, enhancing adaptation to the job and fostering intention to remain among advanced beginner nurses.

Purpose: This study aimed to determine the impact of work environment, missed nursing care, and non-nursing tasks on the job stress of hospital nurses. Methods: A descriptive, cross-sectional survey of 164 nurses working in three hospitals was conducted. Data were collected using structured questionnaires and analyzed using descriptive statistics, t-test, one-way analysis of variance, Pearson’s correlation coefficient, and multiple regression analysis using SPSS/WIN 26.0. Results: Hospital Nurses' job stress was negatively correlated with the work environment (r=-.37, p<.001). Job stress had a statistically significant positive correlation with non-nursing tasks (r=.34, p<.001);however, it had no significant correlation with missed nursing care (r=.05, p=.552). Regression model analysis revealed that the variables associated with job stress were non-nursing tasks (β=.29, p<.001) and the foundation for quality nursing care (β=-.25, p=.034), which are subdomains of the work environment. In contrast, missed nursing care was not determined to be a significant factor. Conclusion To effectively manage nurses' job stress, nurse managers should provide higher quality care by reducing the number of non-nursing tasks and creating a positive work environment, especially by providing adequate nursing workforce and material support.

Purpose: This study aimed to explore the influence of teamwork and grit on job adaptation and intention to remain among advanced beginner nurses. Methods: A cross-sectional descriptive research design employing a survey method was used. The study involved 203 advanced beginner nurses with 13~36 months of experience working at a tertiary hospital. Collected data were analyzed using descriptive statistics, independent t-tests, one-way ANOVA, Pearson's correlation coefficients, and hierarchical regression analysis. Results: Job adaptation positively correlated with teamwork (r=.58, p<.001) and grit (r=.65, p<.001). Intention to remain was also positively correlated with teamwork (r=.32, p<.001) and grit (r=.59, p<.001). A hierarchical multiple regression model including control variables, teamwork, and grit accounted for 64% of the factors influencing job adaptation. Another hierarchical multiple regression model, incorporating control variables, teamwork, and grit, explained 44% of the factors influencing the intention to remain. Among the related factors, grit emerged as the most influential in regarding job adaption and intention to remain. Conclusion These findings underscore the importance of establishing a support system to bolster grit, enhancing adaptation to the job and fostering intention to remain among advanced beginner nurses.