Eating disorders, primary care, and stigma: an analysis of research trends and patterns

Eating disorders (EDs) are a growing concern affecting millions worldwide. Early detection and treatment are crucial, but stigma can prevent people from seeking help. Primary care providers can play a critical role in early detection by coordinating care with other professionals. Understanding the research landscape on EDs, primary care, and stigma is essential for identifying knowledge gaps to direct future research and improve management. In this study, we aimed to analyze the scientific trends and patterns in research about EDs, primary care, and stigma. A bibliometric analysis was conducted using the Web of Science database to collect articles published between May 1986 and May 2023. Bibliometric indicators were utilized to examine authorship, collaboration patterns, and influential papers. Topic analysis was performed to identify stigma-related terms within the dataset. A total of 541 research articles were analyzed, and it was found that the average number of publications per year has increased linearly from nearly zero in 1986 to 41 in 2022. One of the study’s main findings is that despite this linear increase over the years, the subject of stigma did not take a prominent place in the literature. Only a few stigma concepts could be identified with the topic analysis. The authors in the field are also interested in; screening, neurotic symptoms, training, adolescent, obesity-related conditions, and family. One-third of all publications were from 15 journals. However, only two of them were primary healthcare journals. Leading authors’ collaborations were another critical finding from the network analysis. This may help to expand primary care related EDs research to end the mental health stigma. This study provides insights into the research trends and patterns regarding eating disorders, primary care, and stigma. Our findings highlight the need to address primary care’s impact and stigma on EDs. The identified research gaps can guide future studies to improve the prevention, diagnosis, and treatment of eating disorders in primary care settings

CBS-coğrafi bilgi sistemi aracılığıyla veri tabanı oluşturulması ve coğrafya dersinde kullanılması

TEZ6715Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2007.Kaynakça (s.56-58) var.xii, 67 s. ; 29 cm.…Bu çalışma Ç.Ü. Bilimsel Araştırma Projeleri Birimi Tarafından Desteklenmiştir. Proje No

Human body carbohydrate and fat modeling by using artificial neural networks.

TEZ13122Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2020.Kaynakça (s. 84-93) var.XI, 96 s. :_res. (bzs. rnk.), tablo ;_29 cm.Bu çalışmada yapay sinir ağları kullanılarak kardiyo pulmoner egzersiz testleri (KPET) verileri, 6 dakika yürüme testi verileri ve antropometrik veriler modellenmiştir. Çalışmaya 20-30 yaş arasındaki 44 sedanter erkek birey katılmıştır. Katılımcılara 4 adet egzersiz testi uygulanmış ve bireylerin antropometrik ölçümleri yapılmıştır. Bu ölçümler maksimal kardiyopulmoner egzersiz testi, en yüksek yağ oksidasyon hızı aralığı tespiti testi (yağmaks), 40 dk yürüme ve 6 dakika yürüme testi olmak üzere 4 farklı egzersiz testinden oluşmuştur. Yapılan yapay sinir ağları (YSA) modellerinde, uygulanabilmesi için ciddi bir fiziksel alt yapıya, gelişmiş laboratuvar donanımına ve eğitimli personele gereksinim duyulan KPET sonuç parametreleri başarılı bir şekilde nispeten uygulanması daha kolay 6 dakika yürüme testi ve antropometrik ölçüm verilerinden tahmin edilmesi amaçlanmıştır. Yapılan YSA modellerinde eğitim, doğrulama ve test kümelerine ilişkin tahmin değerleri ile gerçek değerler arasındaki uyum 0,98 ve üzeri bulunmuştur. Elde edilen sonuçlarda KPET Yağmaks testi ve KPET 40 dakika yürüme testi verilerinden elde edilen CHO ve Fat (kkal/day) parametreleri başarıyla tahmin edilmiştir. KPET’lerinin yapılamadığı durumlarda, modellenmiş YSA’lar ile birinci basamak sağlık hizmeti seviyesinde kullanılabilecek daha basit ve ekonomik alternatiflerden olan 6 dakika yürüme testi ve antropometrik ölçümlerin kullanılabileceği ortaya konulmuştur.In this study, cardio pulmonary exercise tests (CPET) data, 6-minute walk test data and anthropometric data were modeled using artificial neural networks. 44 sedentary male individuals ages ranging from 20 to 30 were participated in the study. Following anthropometric measurements all Participants were applied to 4 different exercise tests which including maximal cardiopulmonary exercise test, highest fat oxidation rate detection test (Fatmax), 40 minutes walking and 6 minutes walking test. In artificial neural network models, it is aimed to estimate the data of CPET, by using more simpler and easily applied 6 minutes walk test and anthropometric measurement data which required serious physical infrastructure, advanced laboratory equipment and trained personnel in the past. In the ANN models, the correlation between the predicted values of the training, verification and test sets and the actual values was found to be 0.98 and above. 40 minutes walking test data parameters including CHO and Fat (kkal / day) were successfully estimated in the results as well as by using parameters from CPET Fatmaks test and CPET 40 minutes walking test data. In cases where CPET are not available, 6-minute walk test and anthropometric measurements, which are one of the simpler and more economical alternatives that can be used at the primary health care level, are modeled