Sinem Kabak

This study aimed to predict nicotine dependence levels among university students in Türkiye by modeling the Fagerström Test for Nicotine Dependence (FTND) score as a continuous outcome using penalized regression and machine learning (ML) approaches. A cross-sectional study was conducted with a stratified sample of 1,120 university students, examining 24 psychological, behavioral, motivational, and sociodemographic variables. Six predictive models were compared: linear regression, Least Absolute Shrinkage and Selection Operator (LASSO), Ridge regression, Elastic Net, Support Vector Regression (SVR), and tuned extreme gradient boosting (XGBoost). Penalized regression models and the tuned XGBoost algorithm demonstrated largely comparable predictive performance. XGBoost achieved the highest predictive accuracy (R² = 0.621; adjusted R² = 0.575) and the lowest RMSE (1.678). However, the LASSO model yielded nearly identical performance (R² = 0.620; RMSE = 1.680) while offering greater interpretability through embedded variable selection. Given the negligible difference in predictive accuracy, LASSO was selected as the primary model due to its clinical utility and ability to identify modifiable risk factors. The LASSO model explained approximately 59% of the variance in FTND scores and identified 11 significant predictors, including emotional symptoms (e.g., irritability and depressive mood), habitual smoking, intention to quit, daily cigarette consumption, and family smoking. This study highlights the value of interpretable modeling approaches and underscores that nicotine dependence is shaped by a complex interplay of psychosocial, behavioral, and demographic determinants in addition to physiological factors.

Sağlık sistemi içinde önemli bir paya sahip olan hastanelerin finansal sağlıklarını korumaları önem arz etmektedir. Hastanelerde finansal sürdürülebilirliğin sağlanması için düzenli aralıklarla finansal performanslarının değerlendirilmesi ve gerekli durumlarda önleyici tedbirlerin alınması gerekmektedir. Bu nedenle bu çalışmada, Borsa İstanbul’da faaliyet gösteren iki hastane grubunun finansal performanslarının incelenmesiyle sağlık yöneticilerine ve literatüre katkı sağlanması amaçlanmıştır. Analizde 2022-2024 yıllarına ait veriler kullanılmış olup söz konusu veriler Kamu Aydınlatma Platformu (KAP) üzerinden elde edilmiştir. Elde edilen veriler, finansal analiz türlerinden DuPont analizi ile değerlendirilmiştir. DuPont analizi sonuçlarına göre MLP Care Sağlık Grubu A.Ş.’nin kârlılık oranlarının Lokman Hekim Sağlık Grubu A.Ş.’ye göre daha yüksek olduğu belirlenmiştir. Ayrıca her iki hastane grubunun öz sermaye kârlılığında düşüş eğilimi tespit edilmiştir.

Medical tourism is a mobility of people traveling from one place to another for medical treatment. Healthcare organizations want to determine the wishes and expectations of medical tourists and produce products accordingly. The most important issue here is how to achieve this. Moreover, what are the demands and expectations of consumers and how they change in constantly changing environmental conditions. Marketing activities carried out in this context have an important role in the success of organizations. Since medical tourism marketing is a branch of the service sector, it also has characteristics related to the marketing of services. The aim of this study is to find an answer to the question of how the extended marketing mix elements bring a perspective to medical tourism. In this context, the medical tourism literature has been examined through a comprehensive review method. In this study, "Product", "Price" and "People" are considered as critical elements in medical tourism marketing strategies. It is important to address the concept of medical tourism with the elements of the extended marketing mix in this way in order to understand all elements in the best way. In this way, the application of the 7Ps can lead to strategic marketing insights for both healthcare providers and national healthcare services.