Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Purpose: This study examined the direct effects of fetal attachment, social support, and antepartum depression on anxiety in pregnant women with high-risk pregnancy-related conditions and their husbands. Furthermore, it aimed to explore the serial mediation effects of social support and antepartum depression in the relationship between fetal attachment and anxiety. Methods: A survey-based study was conducted among pregnant women diagnosed with high-risk pregnancy conditions at 24–32 weeks and their husbands, recruited from a pregnant women’s online community between January 20, 2021 and July 20, 2022. Data were collected from 294 individuals (147 couples) using self-report questionnaires. Correlations between variables were analyzed using the IBM SPSS software ver. 26.0 (IBM Corp.), and the mediation effects were assessed using the PROCESS macro, model 6. Results: In the maternal model, maternal-fetal attachment directly affected anxiety (p=.005), with antepartum depression partially mediating this relationship (95% confidence interval [CI], –0.26 to –0.01). In the paternal model, paternal-fetal attachment had no direct effect on anxiety (p=.458). However, social support and antepartum depression fully mediated the relationship between paternal-fetal attachment and anxiety (95% CI, –0.14 to –0.03). Conclusion The findings indicate that social support in the relationship between fetal attachment and depression in high-risk pregnant women and their partners can have direct or indirect effects on the negative emotions of high-risk pregnant couples. It is necessary to assess the level of anxiety in couples experiencing high-risk pregnancies and provide comprehensive nursing interventions that address fetal attachment, social support, and antepartum depression in order to reduce anxiety.

Purpose: This study examined the direct effects of fetal attachment, social support, and antepartum depression on anxiety in pregnant women with high-risk pregnancy-related conditions and their husbands. Furthermore, it aimed to explore the serial mediation effects of social support and antepartum depression in the relationship between fetal attachment and anxiety. Methods: A survey-based study was conducted among pregnant women diagnosed with high-risk pregnancy conditions at 24–32 weeks and their husbands, recruited from a pregnant women’s online community between January 20, 2021 and July 20, 2022. Data were collected from 294 individuals (147 couples) using self-report questionnaires. Correlations between variables were analyzed using the IBM SPSS software ver. 26.0 (IBM Corp.), and the mediation effects were assessed using the PROCESS macro, model 6. Results: In the maternal model, maternal-fetal attachment directly affected anxiety (p=.005), with antepartum depression partially mediating this relationship (95% confidence interval [CI], –0.26 to –0.01). In the paternal model, paternal-fetal attachment had no direct effect on anxiety (p=.458). However, social support and antepartum depression fully mediated the relationship between paternal-fetal attachment and anxiety (95% CI, –0.14 to –0.03). Conclusion The findings indicate that social support in the relationship between fetal attachment and depression in high-risk pregnant women and their partners can have direct or indirect effects on the negative emotions of high-risk pregnant couples. It is necessary to assess the level of anxiety in couples experiencing high-risk pregnancies and provide comprehensive nursing interventions that address fetal attachment, social support, and antepartum depression in order to reduce anxiety.

Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Purpose: This study examined the direct effects of fetal attachment, social support, and antepartum depression on anxiety in pregnant women with high-risk pregnancy-related conditions and their husbands. Furthermore, it aimed to explore the serial mediation effects of social support and antepartum depression in the relationship between fetal attachment and anxiety. Methods: A survey-based study was conducted among pregnant women diagnosed with high-risk pregnancy conditions at 24–32 weeks and their husbands, recruited from a pregnant women’s online community between January 20, 2021 and July 20, 2022. Data were collected from 294 individuals (147 couples) using self-report questionnaires. Correlations between variables were analyzed using the IBM SPSS software ver. 26.0 (IBM Corp.), and the mediation effects were assessed using the PROCESS macro, model 6. Results: In the maternal model, maternal-fetal attachment directly affected anxiety (p=.005), with antepartum depression partially mediating this relationship (95% confidence interval [CI], –0.26 to –0.01). In the paternal model, paternal-fetal attachment had no direct effect on anxiety (p=.458). However, social support and antepartum depression fully mediated the relationship between paternal-fetal attachment and anxiety (95% CI, –0.14 to –0.03). Conclusion The findings indicate that social support in the relationship between fetal attachment and depression in high-risk pregnant women and their partners can have direct or indirect effects on the negative emotions of high-risk pregnant couples. It is necessary to assess the level of anxiety in couples experiencing high-risk pregnancies and provide comprehensive nursing interventions that address fetal attachment, social support, and antepartum depression in order to reduce anxiety.

Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Purpose: This study examined the direct effects of fetal attachment, social support, and antepartum depression on anxiety in pregnant women with high-risk pregnancy-related conditions and their husbands. Furthermore, it aimed to explore the serial mediation effects of social support and antepartum depression in the relationship between fetal attachment and anxiety. Methods: A survey-based study was conducted among pregnant women diagnosed with high-risk pregnancy conditions at 24–32 weeks and their husbands, recruited from a pregnant women’s online community between January 20, 2021 and July 20, 2022. Data were collected from 294 individuals (147 couples) using self-report questionnaires. Correlations between variables were analyzed using the IBM SPSS software ver. 26.0 (IBM Corp.), and the mediation effects were assessed using the PROCESS macro, model 6. Results: In the maternal model, maternal-fetal attachment directly affected anxiety (p=.005), with antepartum depression partially mediating this relationship (95% confidence interval [CI], –0.26 to –0.01). In the paternal model, paternal-fetal attachment had no direct effect on anxiety (p=.458). However, social support and antepartum depression fully mediated the relationship between paternal-fetal attachment and anxiety (95% CI, –0.14 to –0.03). Conclusion The findings indicate that social support in the relationship between fetal attachment and depression in high-risk pregnant women and their partners can have direct or indirect effects on the negative emotions of high-risk pregnant couples. It is necessary to assess the level of anxiety in couples experiencing high-risk pregnancies and provide comprehensive nursing interventions that address fetal attachment, social support, and antepartum depression in order to reduce anxiety.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Purpose: This study aimed to explore nurses’ experience in caring for COVID-19 patients. Methods: A total of 10 nurses working in a COVID-19 ward of a public hospital in South Korea were recruited using purposeful sampling. Individual telephone interviews were conducted and then transcribed verbatim. Data were analyzed using qualitative content analysis. Results: Two categories of nurses’ experience in caring for COVID-19 patients emerged; “unstable psychological status” and “adaptation and self-esteem”. “Shortage of staff due to the increase in infected people”, “poor environment due to the urgent construction of a COVID-19 ward”, “unstable operating system”, and “excessive demands and verbal abuse from patients” were “obstacles”, while “cooperation and consideration between colleagues” and “interest and support from the manager” were found to be “sources to boost morale” for nurses in caring for COVID-19 patients. Conclusion This study can be fundamental data for a deeper understanding of the experiences and challenges faced by frontline nurses caring for COVID-19 patients. It is necessary to provide psychological support for nurses and establish a well-structured nursing care system in order to fight a pandemic such as COVID-19.

Surveys on helminthic fauna of the nutria, Myocastor coypus, have seldom been performed in the Republic of Korea. In the present study, we describe Strongyloides myopotami (Secernentea: Strongyloididae) recovered from the small intestine of feral nutrias. Total 10 adult nutrias were captured in a wetland area in Gimhae-si (City), Gyeongsangnam-do (Province) in April 2013. They were transported to our laboratory, euthanized with ether, and necropsied. About 1,300 nematode specimens were recovered from 10 nutrias, and some of them were morphologically observed by light and scanning electron microscopies. They were 3.7-4.7 (4.0+/-0.36) mm in length, 0.03-0.04 (0.033) mm in width. The worm dimension and other morphological characters, including prominent lips of the vulva, blunted conical tail, straight type of the ovary, and 8-chambered stoma, were all consistent with S. myopotami. This nematode fauna is reported for the first time in Korea.
Animals ; Republic of Korea/epidemiology ; Rodent Diseases/epidemiology/*parasitology ; Rodentia ; Strongyloides/*isolation & purification ; Strongyloidiasis/epidemiology/parasitology/*veterinary