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Wyświetlanie 1-2 z 2
Tytuł:
Factors affecting the decision to change the family physician
Autorzy:
Topak, Nevruz Yildirim
Demirci, Hakan
Powiązania:
https://bibliotekanauki.pl/articles/551751.pdf
Data publikacji:
2019
Wydawca:
Stowarzyszenie Przyjaciół Medycyny Rodzinnej i Lekarzy Rodzinnych
Tematy:
education
gender identity
physicians
family
patient satisfaction.
Opis:
Background. Some studies on the decision of patients to choose their primary healthcare physician demonstrate that the ability to choose their physician is associated with increased patient satisfaction, confidence in the doctor and quality healthcare. Objectives. The study was aimed at evaluating factors effecting the decision to change the family physician. Material and methods. In the study, a questionnaire was used to examine the socio-demographic characteristics of the individuals, and the EUROPEP scale was used to measure the satisfaction with primary health services. Moreover, the Individual Innovativeness Scale was used in order to evaluate the innovativeness of individuals. Results. In people who apply to change their family physician, satisfaction with the previous family physician was found to be 69%. Distance (52.7%), education (25.8%) and gender (16%) were declared as the most important reasons to change the family physician. An individual’s innovation seeking behavior did not affect on their decisions to change the physician. Conclusions. In the present study, patient satisfaction was lower than the results reported in previous studies. Distance, education and gender are at the forefront in family physician preference. Patients prioritize receiving service from trained family physicians. These issues should be taken into account while planning the future of family practice.
Źródło:
Family Medicine & Primary Care Review; 2019, 2; 174-179
1734-3402
Pojawia się w:
Family Medicine & Primary Care Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques
Autorzy:
Üneş, Fatih
Taşar, Bestami
Demirci, Mustafa
Zelenakova, Martina
Kaya, Yunus Ziya
Varçin, Hakan
Powiązania:
https://bibliotekanauki.pl/articles/2069941.pdf
Data publikacji:
2021
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
prediction
neuro-fuzzy
sediment rating curve
support vector machine
suspended sediment
Opis:
Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVM-PK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values.
Źródło:
Rocznik Ochrona Środowiska; 2021, 23; 117--137
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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