This single-group quasi-experimental study was conducted with a pretest-posttest design to measure changes in the knowledge and awareness levels of religious officials in the context of an education program about addiction. Data were collected using the Substance Addiction Awareness Scale and the Substance Addiction Knowledge Test before and after the education given to the participants. Paired-samples t-tests were used in the analyses of the data. After the education program, significant increases were seen in the scores of the participants on the overall Substance Addiction Awareness Scale and all its dimensions except for the personal attitudes and opinions dimension. The results of the Substance Addiction Knowledge Test showed that the knowledge levels of the participants about addiction increased after the education program. The results of this study showed that providing a group of professionals who are in contact with society with education about addiction could increase the awareness and knowledge levels of these professionals.
Background: Pain in children is one of the mostcritical symptoms that occur due to illness, trauma,and medical interventions and should be managedObjectives: This study was planned to develop theChildren's Pain Management Self-Efficacy Scale forPediatric Nurses and examine its Turkishpsychometric properties.Material and methods: The sample of thismethodological study consisted of 292 pediatricnurses. A sociodemographic information form andthe Children's Pain Management Self-Efficacy Scalefor Pediatric Nurses (PNCPM-Self Efficacy) wereused to collect data. Numbers, percentages, andmean scores were used for the descriptive data of thenurses, and content validity was analyzed usingexplanatory and confirmatory factor analyses,Cronbach’s α, item-total correlation, and split-half analysis. The scale consisted of 26 items and threesub-dimensions. Cronbach’s α value of the overallscale and sub-dimensions were 0.96, 0.92, 0.91, and091, respectively.Results:The scale explained 62.3% of the totalvariance. The first sub-dimension explained 24.5%of the total variance, the second 21.9%, and the third15.9%. The Goodness of Fit Indexes was found to begreater than 0.90, RMSEA less than 0.08, and otherfit indexes greater than 0.80.Conclusion:This scale is a valid and reliable tool fordetermining the self-efficacy levels of pediatricnurses regarding pain management in children.Keywords: Pediatric nursing, pain management,self-efficacy, scale development, psychometricproperties.
Background: The Omicron variant of SARS-CoV-2 exhibits higher transmissibility compared to previous variants. This retrospective, single-center study aimed to evaluate the relationship between vaccination status and mortality among patients infected with the Omicron variant. Methods: This retrospective analysis included 584 patients diagnosed with the Omicron variant of SARS-CoV-2 between January and March 2022 at Bozyaka Training and Research Hospital, Türkiye. Demographic characteristics, comorbidities, laboratory findings, computed tomography (CT) severity scores, vaccination status, and mortality outcomes were assessed. Multivariate logistic regression was used to adjust for potential confounding factors, including age, sex, and comorbidities. Results: Among the 584 patients, 280 were unvaccinated and 304 vaccinated. Mortality was 21% in unvaccinated patients (mean age 56±17) and 10% in vaccinated patients (mean age 62±15). After adjustment for age and Charlson Comorbidity Index, vaccination remained independently associated with reduced mortality (p<0.05). Unvaccinated patients demonstrated higher inflammatory markers, elevated CT severity scores, and greater ICU admission rates (34% vs. 16%). Conclusion: Vaccination significantly reduced mortality and lung involvement among patients infected with the Omicron variant. Despite reports of milder disease, Omicron remains clinically significant, particularly in unvaccinated individuals.
Background: Malnutrition and sarcopenia are common geriatric syndromes strongly associated with morbidity, mortality, and functional decline. This study aimed to assess the prevalence of chronic diseases, geriatric syndromes, nutritional status, and nutritional support use among older adults residing in private nursing homes in Izmir, Turkey. Methods: A cross-sectional study was conducted between July 1 and December 31, 2023, including 342 individuals aged ≥65 years living in private long-term care facilities. Sociodemographic characteristics, chronic diseases, nutritional status, and medication use were collected through structured questionnaires and face-to-face interviews with participants or healthcare staff. Nutritional status was assessed using the Mini Nutritional Assessment (MNA®), and sarcopenia risk was evaluated with the SARC-F questionnaire. Results: Of the participants, 270 (67.3%) were women and 112 (32.7%) men, with a mean age of 80.2 ± 8.5 years (range: 65–98). The prevalence of malnutrition was 45.2% in women and 41.1% in men, while the risk of malnutrition was 42.2% and 29.5%, respectively. Neuropsychiatric disorders were the most common chronic diseases (70.7%) and geriatric syndromes (69.9%). Oral nutritional supplements were used by 52.0% of participants, tube feeding by 8.2%, and parenteral nutrition by 1.5%. Malnourished individuals had significantly lower MNA scores (10.5 ± 4.6) and higher SARC-F scores (7.6 ± 2.3). Malnutrition was significantly associated with psychiatric and neurological disorders (p < 0.001), cardiovascular diseases (p = 0.034), and diabetes mellitus (p < 0.001), but not with hypertension, rheumatologic, gastrointestinal diseases, or cancer. Conclusions: Malnutrition and sarcopenia are highly prevalent among older adults in nursing homes. The prevalence of malnutrition increased progressively from residents receiving oral nutritional supplements to those requiring tube and parenteral feeding. A moderate negative correlation was found between MNA and SARC-F scores. Early identification and targeted nutritional interventions are critical to improving outcomes in this population.
Purpose: The utilisation of automated systems for the purpose of zonal segmentation has the potential to enhance the efficiency and standardisation of magnetic resonance imaging (MRI) examinations. Furthermore, the delineation of the total gland and zonal boundaries prior to the application of magnetic resonance imaging (MRGB) has been demonstrated to enhance the sensitivity of targeted biopsies. The aim of this study is to automatically segment the prostate gland, transitional zone (TZ) and periferal zone (PZ) on prostate Magnetic Resonance Imaging (MRI) using a U-net based convolutional neural network (CNN). Methods: This retrospective study included a total of 100 patients who underwent screening with a 1.5T MRI device between January and December 2020. The acquired images were evaluated by a senior radiology resident and converted to .nifti format using the MedSeg.ai platform. Prostate and TZ masks were manually traced, while the remaining area (PZ) was automatically segmented by extracting the TZ mask from the prostate mask. A U-net based CNN algorithm with 7 depth layers was developed. Data from 80 patients were used for training the algorithm, with 10 randomly selected for validation. The remaining data from 20 patients were used for testing. Evaluation metrics applied on the test set included accuracy, mean and median Dice Similarity Coefficient (DSC), mean Hausdorff Distance (HSD), Mean Surface Distance (MSD), mean Relative Absolute Volume (RAV). Results: Mean DSC of 0.91 ± 0,03, 0.87 ± 0,06, 0.70 ± 0.16 and median DSC of 0.92, 0.90, 0.75 were obtained for prostate gland, TZ and PZ segmentation respectively. Mean HSD was 8.58, 9.52, 18.78, MSD was 0.92, 0.84, 1.30 and mean RAV was 3.51, 9.87, 70.57 for the segmentation of aforementioned structures. Conclusion: The developed U-net algorithm performed better in segmenting the prostate and TZ than in previous studies. While the success rate of PZ segmentation was lower, this could be attributed to various factors, as indicated by state-of-the-art methods in deep learning. This study highlights AI's vital role in automating prostate segmentation.
