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    Development of the self-efficacy scale in caregivers of Duchenne muscular dystrophy patients
    (Ios Press, 2024) Turken, Askeri; Capar, Hasim; Cakmak, Cuma; Kurt, Mehmet Emin; Mentes, Nurettin
    BACKGROUND: It is important to measure the self-efficacy knowledge of the caregiver of Duchenne muscular dystrophy (DMD) patients in order to overcome the problems that arise and carry out the care process in a healthy manner. OBJECTIVE: This research was carried out to develop a self-efficacy scale in caregivers of individuals with DMD. METHODS: The study was conducted with 99 volunteer DMD caregivers to evaluate the psychometric properties of the developed scale. Exploratory Factor Analysis (EFA) was performed with the SPSS 25 Package Program to determine the factors of the scale. Confirmatory Factor Analysis (CFA) analysis was performed with AMOS 23 to confirm the factors obtained by EFA. Cronbach's alpha coefficient was used for the internal consistency of the DMD-CSES. RESULTS: A valid and reliable scale was obtained to measure the self-efficacy of caregivers of DMD patients. CONCLUSION: Although some scales have been developed to evaluate the care burden of family members who care for patient-centered symptoms and functional changes in patients with DMD, there is no single scale that adequately describes the conditions and resources of caregivers on a global scale. The search for a definitive scale is expected to continue until a definitive treatment for the disease is found. Developing a valid and reliable scale to identify the self-efficacy, knowledge, skills and resources of caregivers with a common perspective of physicians and health management team centred on patients with DMD will be effective in practice.
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    Estimation of service length with the machine learning algorithms and neural networks for patients who receiving home health care
    (Pergamon-Elsevier Science Ltd, 2023) Mentes, Nurettin; Cakmak, Mehmet Aziz; Kurt, Mehmet Emin
    The main purpose of the study is to develop an estimation model using machine learning algorithms and to ensure the effective and efficient implementation of home health care service planning in hospitals with these algorithms. The necessary approvals for the study were obtained. The data set was created by obtaining patient data (except for data such as Turkish Republic identification number) from 14 hospitals providing Home Health Care Services in the city of Diyarbakir. The data set was subjected to necessary pre-processing and descriptive statistics were applied. For the estimation model, Decision Tree, Random Forest and Multi-layer Perceptron Neural Network algorithms were used. It was found that the number of days of home health care service, which the patients received, varied depending on their age and gender. It was observed that the patients were generally in the disease groups that required Physiotherapy and Rehabilitation treatments. It was determined that the length of service for patients can be predicted with a high reliability rate (Multi-Layer Model Acc: 90.4%, Decision Tree Model Acc: 86.4%, Random Forest Model Acc: 88.5%) using machine learning algorithms. In the light of the findings and data patterns obtained in the study, it is thought that effective and efficient planning will be made in terms of health management. In addition, it is believed that estimating the average length of service for patients will contribute to strategic planning of human resources for health, and to reducing medical consumables, drugs and hospital expenses.
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    Validity and reliability of the Pandemic Fatigue Scale (PFS) in the Turkish population
    (Ios Press, 2023) Kurt, Mehmet Emin; Capar, Hasim; Cakmak, Cuma; Turken, Askeri; Mentes, Nurettin
    BACKGROUND: The measures developed to fight the COVID-19 pandemic caused fear, stress and anxiety in people over time. It was reported that pandemic fatigue, associated with the gradual loss of motivation to follow the implemented protective measures, emerged in societies. OBJECTIVE: This cross-sectional-methodological study aimed to validate the Turkish version of the Pandemic Fatigue Scale, developed by Lilleholt et al. (2020). METHODS: A web-based questionnaire was conducted to examine the validity and reliability of the Turkish version of the PFS. 1149 participants from all regions in Turkey participated. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed. RESULTS: As a result of the KMO and Bartlett's Test of Sphericity, the scale was suitable for the factor analysis. According to EFA, the scale has two sub-factors. The first sub-factor explained 48.7%, and the second sub-factor explained 16.7% of the total variance. Factor loadings of items varied between 0.67 and 0.89. CFA shows that acceptable fit values were obtained for the RMSEA, GFI, AGFI, CFI, NFI and IFI fit indices. CONCLUSIONS: The results support that PFS is a valid and reliable screening tool that can be used to measure the phenomenon of pandemic fatigue.