PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/8

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Psychometrics of Stanford Presenteeism Scale-Short Form in Turkish
(Cordus, 2022) Teoman, Ezgi; Seren, Arzu Kader Harmanci
AIM: Presenteeism means that employees feel obliged to go to work even if there is a real problem that they cannot work.The main purpose is to to adapt the "Stanford Presenteeism Scale-Short Form" into Turkish on Nurses. METHOD: This is a methodological study. The study sample included the nurses working at the medical and surgical clinics of two public hospitals in 2017 in Istanbul. A total of 290 nurses participated in the study. Language, content, construct validities, total item correlation analysis, Kaiser Meyer Olkin, Bartlett tests, confirmatory and explanatory factor analysis (EFA), stability, and Cronbach's alpha reliability analyses were tested. RESULTS: The content validity index of the scale was.92. Two items that have correlation values below.40 were removed from the Turkish form. Cronbach's alpha internal consistency coefficient was.762. The structure of the four-item and single-factor Turkish form was confirmed. CONCLUSION: Stanford Presenteeism Scale-Short Form is a valid and reliable tool for the nurses in Turkey. It is recommended to be used among nurses in different studies. Hospital and nursing care service managers should deal with "presenteeism," since it is becoming a critical health human resource workforce issue. Health care managers may use this tool to evaluate the presenteeism level of their employees.
Emergency Department Nurses’ Knowledge and Practices Related to Extravasation Injuries of Non-Cytotoxic Medications
(Turkish Association of Trauma and Emergency Surgery, 2025) Kuğu, Emre; Akyüz, Nuray
BACKGROUND: Extravasation of non-cytotoxic medications can lead to serious complications such as pain, tissue necrosis, limb loss, and even death. This descriptive cross-sectional study aims to assess the knowledge levels of emergency department (ED) nurses regarding extravasation incidents involving non-cytotoxic medications and to highlight the importance of effective management and prevention. METHODS: The study was conducted in the EDs of three hospitals in Istanbul, Türkiye, between November 19, 2020 and December 31, 2020. A total of 100 ED nurses participated in the study. Inclusion criteria required nurses to be working full-time in the EDs during the study period and to provide written and verbal consent. The study utilized a survey to assess sociodemographic characteristics, knowledge of non-cytotoxic medications (e.g., epinephrine), symptoms of extravasation, prevention strategies, and intervention practices. RESULTS: The mean age of the nurses was 29.43 years, with 57% female and 73% holding a bachelor’s degree. Among participants, 52% had 0-3 years of ED experience. Ninety-one percent reported not receiving education on extravasation after graduation, and 82% indicated no extravasation protocol was in place at their workplace. Knowledge about non-cytotoxic medications causing extravasation significantly increased with ED experience (p=0.035). Nurses in units with an extravasation protocol had significantly higher knowledge levels (p=0.007). Female nurses demonstrated better knowledge of extravasation symptoms than male nurses (p=0.012). Nurses with a bachelor’s or higher degree had significantly better knowledge than others (p=0.015). The knowledge rate for the extravasation care protocol was 64%, with the most recognized protocol item being “immediately stop the infusion” (97%) and the least recognized being “aspirate the medication not to exceed 3-5 mL” (33%). Strong correlations were found between non-pharmacological factors and knowledge of non-cytotoxic medications (r=0.601; p<0.001), as well as between knowledge of extravasation care protocols and non-pharmacological factors (p<0.001). CONCLUSION: The study highlights the need for targeted education and the establishment of institutional protocols for managing and preventing extravasation in EDs. Nurses' knowledge significantly impacts their adherence to prevention and care protocols. To ensure patient safety, it is important to provide ongoing education and implement evidence-based intervention protocols for the management of extravasation in ED settings. © 2025 Elsevier B.V., All rights reserved.
Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia Coli Isolates: A Retrospective General Surgery Study
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tolan, H.K.; Aydın, İ.; Tanyildizi-Kökkülünk, H.; Karakuş, M.; Akkaya, Y.; Kaya, O.; Işman, F.K.
Background/Objectives: Escherichia coli is one of the leading causes of surgical site infections (SSIs) and poses a growing public health concern due to its increasing antimicrobial resistance. High rates of extended-spectrum beta-lactamase (ESBL) production among E. coli strains complicate treatment outcomes and emphasize the need for effective surveillance and control strategies. Methods: A total of 691 E. coli isolates from general surgery clinics (2020–2025) were identified using MALDI-TOF MS. Antibiotic susceptibility data and patient variables were cleaned, encoded, and used to predict resistance using the Random Forest, CatBoost, and Naive Bayes algorithms. SMOTE addressed class imbalance, and model performance was assessed through various validation methods. Results: Among the three machine learning models tested, Random Forest (RF) showed the best performance in predicting antibiotic resistance of E. coli, achieving median accuracy, precision, recall, and F1-scores of 0.90 and AUC values up to 0.99 for key antibiotics. CatBoost performed similarly but was less stable with imbalanced data, while Naive Bayes showed lower accuracy. Feature importance analysis highlighted strong inter-antibiotic resistance links, especially among β-lactams, and some influence of demographic factors. Conclusions: This study highlights the potential of simple, high-performing models using structured clinical data to predict antimicrobial resistance, especially in resource-limited clinical settings. By incorporating machine learning into antimicrobial resistance (AMR) surveillance systems, our goal is to support the advancement of rapid diagnostics and targeted antimicrobial stewardship approaches, which are essential in addressing the growing challenge of multidrug resistance. © 2025 by the authors.

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