University of Nicosia

About: University of Nicosia is a education organization based out in Nicosia, Cyprus. It is known for research contribution in the topics: Population & Context (language use). The organization has 988 authors who have published 2765 publications receiving 30748 citations.

Sustainable intercultural and inclusive education: teachers’ efforts on promoting a combining paradigm

Abstract: Educational systems face various challenges: globalisation, the fourth industrial revolution, a global recession, and global mobility. As a result they have to become inclusive of diversity to brin...

Barriers, facilitators, and interventions for medication adherence across chronic conditions with the highest non-adherence rates: a scoping review with recommendations for intervention development.

Abstract: Medication non-adherence (MNA) constitutes a complex health problem contributing to increased economic burden and poor health outcomes. The Medication Adherence Model (MAM) supports that numerous processes are involved in medication adherence (MA). Based on the MAM and guidelines of the World Health Organization (WHO), this scoping review aimed to identify the barriers and facilitators associated with MA, and the behavioral health interventions and techniques among chronic conditions presenting with high non-adherence rates (asthma, cancer, diabetes, epilepsy, HIV/AIDS, and hypertension). PubMed, PsycINFO, and Scopus databases were screened, and 243 studies were included. A mixed methods approach was used to collate the evidence and interpret findings. The most commonly reported barriers to MA across conditions were younger age, low education, low income, high medication cost, side effects, patient beliefs/perceptions, comorbidities, and poor patient-provider communication. Additionally, digitally delivered interventions including components such as medication and condition education, motivational interviewing (MI), and reinforcement and motivational messages led to improvements in MA. This review highlights the importance of administrating multicomponent interventions digitally and personalized to the patients' individual needs and characteristics, responding to the adherence barriers faced. This is the first review examining and synthesizing evidence on barriers and facilitators to MA and behavioral health interventions used for improving MA across chronic conditions with the highest non-adherence rates and providing recommendations to researchers and clinicians. Stakeholders are called to explore methods overcoming barriers identified and developing effective multicomponent interventions that can reduce the high rates of MNA.

Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification.

Abstract: Background and Purpose: Only 1–2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches—a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i–ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv–v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.

Parents' Perceptions of Educational Apps Use for Kindergarten Children: Development and Validation of a New Instrument (PEAU-p) and Exploration of Parents' Profiles.

Abstract: Contemporary mobile technologies offer tablets and smartphones that elicit young children’s active participation in various educational apps, dramatically transforming playing, learning, and communication. Even the most knowledgeable users face difficulties in deciding about the value and appropriateness of the so-called educational apps because of many factors that should be considered. Their importance for children’s attitudes is affected by the perceived positive and negative aspects, which vary across a multiplicity of criteria. Filling the gap in the relevant literature, a new instrument, named PEAU-p (Perceptions about Educational Apps Use–parents), was developed and validated in the present study designed to measure parents’ perception of educational apps for kindergarten pupils. Data (N = 435) were collected via online procedures, and the psychometric properties of PEAU-p were studied via exploratory and confirmatory methods. Principal Components Analysis extracted six factors, namely Usability, Enjoyment, Involvement, Learning, Worries, and Values, which explained 72.42% of the total variance. Subsequently, by implementing Latent Class Analysis based on the above factors, four Clusters (i.e., parents’ Profiles) were extracted corresponding to their perceptions and attitudes towards the educational apps used for kindergarten pupils. Those were named as ‘mild attitude’, ‘negative attitude’, ‘positive attitude’, and ‘indifferent attitude’. This categorization, besides the statistical support, is fully interpretable, and the profiles were associated with certain covariates, such as age, the number of children, knowledge on new technologies, or distal outcomes, e.g., the frequency of using apps, the general position towards apps or their intention to recommend apps use. The findings are discussed within the current research field, investigating the influential role parents play in young children’s media selection and use.