WoS İndeksli Yayınlar / WoS Indexed Publications

Permanent URI for this collectionhttps://hdl.handle.net/11413/6359

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Browsing WoS İndeksli Yayınlar / WoS Indexed Publications by Rights "http://creativecommons.org/licenses/by-nc/3.0/us/"

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    Deep Learning-Based User Experience Evaluation in Distance Learning
    (Springer, 2023) SADIGOV, RAHIM; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR; Çatal, Çağatay
    The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals' financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users' emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.
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    Psychosocial Adaptation to Precocious Puberty: A Nursing Support Program
    (Wiley, 2022) MİRAL, MUKADDES TURAN; Şahin, Nevin Hotun
    Problem: This study aimed to determine the effects of a nursing support program (NSP) based on the Roy Adaptation Model on the psychosocial adaptation of girls with precocious puberty and their mothers. Methods: This study adopted a pre-post design. It included 26 girls diagnosed with precocious puberty and their mothers. Data were collected using a Demographic Information Form; the Child Behavior Checklist for Ages 6-18; and the Depression, Anxiety, and Stress Scale. Participants were then enrolled in a NSP based on the Roy Adaptation Model. The same measures were administered at the end of the support program to the participants. Findings: It was determined that at the beginning of the program, approximately one-third of the mothers had depression, 15% anxiety, and approximately 20% experienced stress. Mothers' anxiety and stress levels and girls' anxiety/depression and total problem scores significantly decreased after the NSP. Conclusions: The NSP designed for this study positively affected the psychosocial problems of girls with precocious puberty and their mothers.
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    Sociodemographic and Clinical Factors Affecting Treatment Adherence in Adults with Attention Deficit and Hyperactivity Disorder
    (Aves, 2022) ERKAN, ARZU; Kılıç, Özge; Semerci, Bengi
    Background: This study aims to explore sociodemographic and clinical factors affecting medication adherence in adults with attention deficit and hyperactivity disorder and elicit dysfunctional domains and comorbidities with a focus on gender differences. Methods: Patients were recruited from 2 specialty clinics using chart records in a natural treatment design. Adult attention deficit and hyperactivity disorder self-report scale, Diagnostic Interview for attention deficit and hyperactivity disorder in adults, was applied. Adherence is defined if the patient declared >= 80% adherence to medication throughout the last 8-12 weeks. Results: From 205 attention deficit and hyperactivity disorder patients (male =112 female = 93 (age (median) min-max = 29 (18-56)), 29% were non-adherent to attention deficit and hyperactivity disorder medication. In the multivariate analysis, having 2 or more comorbid disorders (P = .009), dysfunctions in academic/work (P= .049), and dysfunctions in family and other relationships (P = .047) increased the likelihood of adherence. Adherence rates did not significantly differ between methylphenidate and atomoxetine (P= .405). Women were more likely to have 2 or more comorbid psychiatric disorders (P = .004) and dysfunctions in social relationships (P= .001), free time activities, hobbies (P < .001), self-confidence, and self-image (P < .001). Results: Nearly one-third of adult patients with attention deficit and hyperactivity disorder did not adhere to medication treatment. Comorbid psychiatric disorders and dysfunctions in life domains appear to increase the likelihood of adherence to attention deficit and hyperactivity disorder medications, possibly through increasing motivation for treatment. The effect of cognitive-behavioral therapy on compliance with attention deficit and hyperactivity disorder medication should further be explored with prospective controlled studies. Conclusion: We suggest that future longitudinal studies use objective measures of adherence and confirm the role of dysfunctional life domains and comorbid psychiatric disorders as correlates of medication adherence.