PURPOSE: The purpose of this study was to identify the effects of social support and emotional intelligence in the relationship between emotional labor and burnout among clinical nurses. METHODS: The sample for this study consisted of 382 nurses from four hospitals located in Seoul or Gyunggi Province. Data were analyzed using frequency, percentage, mean, standard deviation, t-test, ANOVA, Scheffe test, Pearson Correlation, Hierarchical Multiple Regression, and Path Analysis. RESULTS: It was found that: (a) emotional labor had a positive effect on burnout, while social support and emotional intelligence had negative effects on burnout; (b) social support and emotional intelligence moderated the relationship between emotional labor and burnout, and (c) social support mediated the relationship between emotional labor and burnout, whereas emotional intelligence did not. CONCLUSION: The results of the study indicate that high levels of support had a buffering effect and mitigated the negative effects of the emotional labor on burnout. Therefore, strategies to enhance social support for nurses are needed and further research needs to be done to refine this study.
Emotional Intelligence

No abstract available.
Artificial Intelligence*

No abstract available.
Artificial Intelligence

No abstract available.
Artificial Intelligence

No abstract available.
Artificial Intelligence

Artificial Intelligence

Artificial Intelligence

Artificial Intelligence

Twenty-six years earlier in their famous chess rematch, an IBM Supercomputer called Deep Blue defeated then-world chess champion Garry Kasparov: it was the first-ever chess match won by a machine, a much celebrated milestone in the field of Artificial Intelligence. Just last year, the World Association of Medical Editors released the “WAME Recommendations on Chatbots and Generative Artificial Intelligence in Relation to Scholarly Publications,” a recognition of not just the expanding applications of AI in scholarly publishing but more so of the accompanying emergence of concerns on authenticity and accuracy. In recognition of this relevant topic, our Vice Editor in Chief, Dr. Cecile Jimeno, provided a well-attended and interesting talk during the last ASEAN Federation of Endocrine Society Convention in Thailand on the “Emerging Issues on the Use of Artificial Intelligence for Scientific Publications.”
Artificial Intelligence

Background: Proper assessment and efficient diagnosis of central nervous system anomalies is essential in antenatal surveillance of pregnant patients. These anomalies are usually associated with genetic syndromes or severe malformations requiring timely intervention and antenatal counseling of the expectant couple. Objective: The study aims to evaluate the agreement of cranial biometric measurements and to determine if there is a significant difference in the time needed to complete the evaluation using standard 2D and semi-automated 5D ultrasound. Methods: An analytical cross-sectional study was employed on 93 women who underwent pelvic ultrasound scans from August to October 2022 in a tertiary hospital. Basic biometric fetal central nervous system (CNS) measurements were acquired using 2D ultrasound followed by 5D CNS ultrasound. Bland-Altman plots were used to evaluate the agreement of the measurements obtained. The difference in the time to completion was determined using independent t-test. Results and Conclusions Our study found that 5D CNS ultrasound measurements showed 96.8% agreement with 2D ultrasound in 90 out of 93 fetuses. The 5D CNS ultrasound takes a shorter time of 90 seconds (s) to completion in comparison to 99 s using the 2D method (p=0.076). Upon stratification of the study population per trimester, in the second trimester, it took 76 s with 5D CNS vs 89 s with 2D, resulting to a statistically significant 13-second difference (p=0.044). In the third trimester, 5D CNS took 105 s vs 108 s with 2D (p=0.614). The time to completion of the scan using this technology is faster when used for second trimester pregnancies but could be affected by fetal-dependent and operator-dependent factors. Therefore, application of this new technology has the potential to improve workflow efficiency after the necessary training on 3D sonography and 5D CNS ultrasound software.
Artificial Intelligence