E-learning brings new dimensions to traditional education. This especially affects countries that, due to many factors, have historically been considered the “talent pool” for the world community. In this study, a model for financing e-education has been developed that is applicable to Russian realities. The model was built around the balance between demand (global politics, economics, and principles of sustainable development) and supply (sources of direct financing). As a result, a key challenge of improving the e-learning financing methodology and models, specifically the efficiency of government spending and private investing, demands the use of new approaches and mechanisms. To improve e-learning financing, a clear understanding of the applied purpose of public and private means is required. Responsibilities for the e-learning outcome of institutions that receive financing are linked to their status. An unclear understanding of these issues is more likely associated with the issue of transparency of financing than with inefficiency. The proposed model allows transforming the “standards” of financing both in the field of e-education and Russian education in general and presents a new vision of participants’ interaction in the educational process, taking into account a set of restrictions and market features.
Background Research predominantly suggests that nurses are at high risk of developing psychopathology. The empirical data show that the occurrence rate of problem-related sleep quality among clinical nurses is high. Therefore, this study was conducted to address the lack of information on the relationship between the coronavirus disease 2019 (COVID-19) pandemic and insomnia. Methods A convenience sample of nurses (n = 680) completed an online survey that included the Insomnia severity index, the COVID-19-related psychological distress scale, the general health questionnaire, neuroticism, dysfunctional beliefs, attitudes about sleep scale, and difficulties in emotion regulation scale. Results The results showed that 35.8% (n = 253) of nurses were classified as individuals with moderate to severe clinical insomnia. The results showed that the psychological distress generated by COVID-19 predicted insomnia (β = .47, SE = 0.02, P < .001, t = 13.27, 95% CI 0.31–0.46). Additionally, the association is mediated by psychopathology vulnerabilities, emotion dysregulation, dysfunctional beliefs about sleep, and neuroticism. Moreover, female nurses exhibited higher levels of insomnia (Cohen’s d = .37), neuroticism (Cohen’s d = 30), psychopathology vulnerability (Cohen’s d = .26), and COVID-19-related psychological distress (Cohen’s d = .23). Conclusion The present study’s findings help to explain how pandemic consequences can be associated with insomnia. Additionally, the findings make a significant contribution to better understanding the role of neuroticism, emotion dysregulation, beliefs, and psychopathology vulnerability in the development of insomnia among nurses. The findings suggest the potential influence of cognitive behavioral therapy for insomnia (CBT-I) and transdiagnostic integrated therapies that could be incorporated into therapeutic programs designed to develop as a way of inhibiting or preventing insomnia among clinical nurses.
The aim of the study was to develop and test the effectiveness of an autonomous learning intelligent platform in post-secondary education. It was conducted on the basis of the Institute of Dentistry named after E.V. Borovsky in I.M. Sechenov First Moscow State Medical University (Moscow, Russia) and Humanitarian and technical academy (Kokshetau, Kazakhstan). This research involved 59 teachers and 390 students, who comprised the total sample of 449 respondents. The experiment consisted of three stages – introductory, experimental, and final. The introductory stage included the distribution of enrolled students into the experimental and control groups. Besides, at the introductory stage, the development of questionnaires directed at identifying students’ and teachers’ readiness to implement autonomous learning was performed. Apart from this, the involved educators were required to fill the learning platform with predetermined training content. The considered intelligent learning platform was developed by programmers by prior agreement with educational institutions under study. The experimental stage was aimed at introducing the designed model of autonomous learning based on the created intelligent platform. The final stage implied surveying of all study participants according to the developed questionnaires. After the introduction of the created autonomous learning model, it was revealed that 51.5% of enrolled teachers were ready for self-directed education at a high level, 20.4% – at a satisfactory level, 18.4% – at a moderate level, and 9.7% – at a low level. Among the students of Sechenov University, 21% of respondents had a high level of readiness for autonomous learning based on intelligent platforms, 27% of students had a sufficient level, 35% – moderate, and 17% – low. Among the Humanitarian and technical academy students, 29% had a high readiness for autonomous learning, 30% were ready at a sufficient level, 25% at a moderate, and 16% at a low level. This study provided an opportunity to use the developed questionnaires and the model of autonomous learning in post-secondary education for further research on the implementation of self-directed training.
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