Supporting University Students During the Pandemic: A Study on The Efficacy of a Mentalizing Online Group Counselling

Background: University counselling services assume a fundamental support function for students who are facing moments of crisis during their academic career. Such services often aim to reduce drop-out rates and achieve improvement in terms of psychological well-being. COVID-19 contagion containment measures have also had an impact on the psychological health of university students and their ability to cope with important developmental tasks. It has become necessary, therefore, to offer online counselling services which has become, however, the means of choice to support students during the university course in the pandemic era, as a complementary intervention to the traditional face-to-face approach. Methods: In a clinical and health psychology perspective, this study aims to analyze the efficacy of 13 online counselling groups involving 66 underachieving students, lagging with their studies. The intervention has adopted the methodology of the Narrative Mediation Path, which aims at promoting mentalization, academic engagement and psychological well-being in order to have an impact on students' academic performance and prevent university dropouts. At the beginning and end of counselling the following measures were administered: a) Reflective Functioning Questionnaire, b) Psychological General Well-Being Index Short Form, c) Academic Performance Inventory, d) University Student Engagement Inventory, e) Group Climate Questionnaire. Results: The results showed that online counselling groups enabled an overall improvement in all the variables considered. Conclusion: Overall, the present study showed the efficacy of the online group counselling service in supporting students during the pandemic period and in coping with the difficulties encountered during the academic career © 2023 by the Author(s);licensee Mediterranean Journal of Clinical Psychology, Messina, Italy. This article is an open access article, licensed under a Creative Commons Attribution 4.0 Unported License

Engagement over telehealth: comparing attendance between dialectical behaviour therapy delivered face-to-face and via telehealth for programs in Australia and New Zealand during the Covid-19 pandemic.

BACKGROUND: While the COVID-19 crisis has had numerous global negative impacts, it has also presented an imperative for mental health care systems to make digital mental health interventions a part of routine care. Accordingly, through necessity, many Dialectical Behaviour Therapy (DBT) programs transitioned to telehealth, despite little information on clinical outcomes compared with face-to-face treatment delivery. This study examined differences in client engagement (i.e. attendance) of DBT: delivered face-to-face prior to the first COVID-19 lockdown in Australia and New Zealand; delivered via telehealth during the lockdown; and delivered post-lockdown. Our primary outcomes were to compare: [1] client attendance rates of DBT individual therapy delivered face-to-face with delivery via telehealth, and [2] client attendance rates of DBT skills training delivered face-to-face compared with delivery via telehealth. METHODS: DBT programs across Australia and New Zealand provided de-identified data for a total of 143 individuals who received DBT treatment provided via telehealth or face-to-face over a six-month period in 2020. Data included attendance rates of DBT individual therapy sessions; attendance rates of DBT skills training sessions as well as drop-out rates and First Nations status of clients. RESULTS: A mixed effects logistic regression model revealed no significant differences between attendance rates for clients attending face-to-face sessions or telehealth sessions for either group therapy or individual therapy. This result was found for clients who identified as First Nations persons and those who didn't identify as First Nations persons. CONCLUSIONS: Clients were as likely to attend their DBT sessions over telehealth as they were face-to-face during the first year of the Covid-19 pandemic. These findings provide preliminary evidence that providing DBT over telehealth may be a viable option to increase access for clients, particularly in areas where face-to-face treatment is not available. Further, based on the data collected in this study, we can be less concerned that offering telehealth treatment will compromise attendance rates compared to face-to-face treatment. Further research is needed comparing clinical outcomes between treatments delivered face-to-face compared delivery via telehealth.

Dupchanchia Model of Students' drop-out control through engagement and appreciation after Covid-19: A Behavioral Policy intervention in the field administration of Bangladesh

Bangladesh is a country which has successfully accomplished the millennium development goals. Upon such accomplishment and with new growing consensus with the global community, the country at present is in pursuit of achieving the sustainable development goals with mostly concentrating on the education sector. However, what impacted the growth and pace of the initiatives was the overwhelming impact of Covid-19 and the lockdown afterwards. Academic institutions remained closed at least for two years which resulted in a compromising number of students after the resumption. This study starts from the identification of a genuine problem with original field level data of the gradually declining number of students. It requires policy intervention centrally and locally. Government of Bangladesh has been deploying some traditional method like vocational and stipend system which is involved with large amount of monetary disbursement. This study found that government like Bangladesh should introduce new or customized education policy which can reduce budgetary involvement and change the choice structure of students. This approach and method has the transferability whereby other similar states can adapt. As a crucial part of local government, I have been working as the chief executive officer of a sub-district called Dupchanchia, and coordinating government departments to implement government policy. After rigorous discussion and brainstorming among the local stakeholders and teachers we uncovered that the students have become demotivated, traumatized and panicked of social engagement and any form of shared activities like classes, games and others. It required us to find out a local policy solution followed by a detailed literature review and primary data collection maneuver. Taking twenty schools into consideration for the study, the project initiated a behavioral policy intervention in ten particular schools and did not interfere with the other ten schools. I engaged local teachers, students and other related stakeholders and continued to use six behavioral tools to change the choice structure of the students of ten selected schools. We observed other ten schools without intervening in their environment and academic atmosphere at all. At the end of the study we collected data through key informant interviews and focus group discussion engaging teachers, peoples' representatives and government officials. Behavioral public policy intervention like nudge and engagement approaches are found to have a positive relation with the change in students number and their performance in the academic and co-curricular activities. This approach may contribute to controlling students' drop out in the lower and lower middle income countries after Covid shock. This policy intervention may have some challenges and limitations which need intensive and rigorous pre-study and prolonged design. Nevertheless, it has unlimited opportunities to be addressed. [ FROM AUTHOR] Copyright of Technium Social Sciences Journal is the property of Technium Press Constanta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

An ecological model of dropout intention in university students during the covid-19 pandemic

Objective: To analyse the drop out intention of university students after six months of home confinement during the covid-19 pandemic using an ecological model. Method: A non-experimental cross-sectional study with an intentional sampling of 1,011 active university students during 2020. Aged between 18 and 54 years (M = 22,6;SD = 4,8;female = 438). We evaluated them with the university dropout questionnaire for students, Depression, Anxiety and Stress Scale, Fear of covid-19 Scales, covid-19 Anxiety syndrome scale, and the Satisfaction with academic changes questionnaire. We performed a descriptive and multivariate analysis of the data. Results: The model explain 28 % of drop out intention among university students during confinement by covid-19 (sensitivity = 86,8 %). Dissatisfaction with academic changes is the main predictor of the model (OR = 0,960;IC 95 % [0,950, 0,959). Other significant predictors are positive and negative inter-actions (i.e., in family, social, and academic environments), negative emotional symptoms, anxiety about covid-19, being older, studying at a private university, and having a family member diagnosed with covid-19. Discussions: Macrosystem changes (i.e., home confinement and online classes) during the first six months of the covid-19 pandemic modified the students' interaction with their proximal systems and new predictors of the intention to drop out emerged (e.g., anxiety about covid-19 and having a family member diagnosed with covid-19), associated with the context of confinement. © 2023, Universidad Simon Bolivar. All rights reserved.

Community College Student Persistence During the COVID-19 Crisis of Spring 2020

Objective: This quantitative study examines the impact of the COVID-19 pandemic on students' persistence at a minority-serving, open-access, public, urban community college in New York City. Specifically, the project looked at factors associated with mid-semester college withdrawals during spring 2020 when the college shifted to remote instruction due to the COVID-19 pandemic. Method: Utilizing data from three spring semesters (spring 2018, 2019, and 2020), four logistic regression models tested the marginal effects of student background and college program factors on mid-semester withdrawal and the moderating effect of spring 2020, the COVID-19 outbreak semester. Results: Findings indicated that the withdrawal rates were higher for new students, men, minoritized students, and part-time students across all three spring semesters. Spring 2020 disproportionally affected part-time students, men, Black students, as well as readmitted students. The greatest increase in the probability of mid-semester college withdrawal was observed for Black men who had been enrolled part-time in spring 2020. Belonging to a highly structured full-time study program protected students from leaving mid-semester, although this protection was weaker in spring 2020 and spring 2019 compared to spring 2018. Contributions: The research highlights the equity gap for Black men at the college and points to additional factors contributing to mid-semester college attrition. The work provides insights into factors that worsened during the COVID-19 pandemic. The study thereby contributes to understanding short-term risk factors for vulnerable student populations and adds to the body of literature on crisis situations in higher education. © The Author(s) 2023.

An Optimized Adaptive Learning Approach Based on Cuckoo Search Algorithm

The rapid expansion of MOOCs (massive open online courses) allows learners to benefit from these courses by removing the barriers that obstruct the right to an open high-quality education. The courses offered on MOOC platforms are often free which has revolutionized this mode of distance learning, especially with the restrictions imposed by the advent of the COVID-19 pandemic. However, even though the number of registrants to MOOCs is quite considerable, only 10% of the learners complete the MOOC and obtain a certification. This phenomenon leads us to dig deeper to wonder about the means to avoid the high dropout rate of learners in such platforms. For this purpose, we suggest in this paper two complementary systems: a preventive system coupled with a proactive system to personalize the learners' pathways according to their specific needs and prior knowledge. The optimization of the pathways will be handled using a metaheuristic optimization algorithm called: Cuckoo Search Algorithm. © 2022 IEEE.

Predicting User Dropout from Their Online Learning Behavior

The Covid-19 pandemic, which required more people to work and learn remotely, emphasized the benefits of online learning. However, these online learning environments, which are typically used on an individual basis, can make it difficult for many to finish courses effectively. At the same time, online learning allows for the monitoring of users, which may help to identify learners who are struggling. In this article, we present the results of a set of experiments focusing on the early prediction of user drop out, based on data from the New Heroes Academy, a learning center providing online courses. For measuring the impact of user behavior over time with respect to user drop out, we build a range of random forest classifiers. Each classifier uses all features, but the feature values are calculated from the day a user starts a course up to a particular day. The target describes whether the user will finish the course or not. Our experimental results (using 10-fold cross-validation) show that the classifiers provide good results (over 90% accuracy from day three with somewhat lower results for the classifiers for day one and two). In particular, the time-based and action-based features have a major impact on the performance, whereas the start-based feature is only important early on (i. e., during day one). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predicting Students' Drop-Out Rate Using Machine Learning Models: A Comparative Study

One of the major challenges facing Private Higher Educational Institutes in India is to reduce drop-out rate of first year students. The problem has got exacerbated post-covid specially for Engineering and management discipline. This research is an empirical study on efficiency and accuracy of various Machine Learning (ML) Prediction algorithms to predict the drop-out rate of students based on dataset available on predictors such as Family size, Study time, Time spent on extra- curricular activities, Time spent on Internet, Health, Absenteeism etc. A comparison of the performance of the ML models based on 'Accuracy' and 'F1 score' (to cater for variations in costs of false positives and false negatives) has been made to identify the best algorithm for given problem. This would help HEIs to identify potential drop-out students beforehand and take course correction measures thus improving retention. The study is conducted for B.Tech First year students with a sample size of 395 students using Logistic regression and K-NN algorithms. This preliminary work could be extended using other ML models such as;Support Vector Machine (SVM), Naïve Bayes, Decision Tree etc. or a combination of in an ensemble fashion in future. © 2022 IEEE.

Drop-out in der Alphabetisierung und Grundbildung Erwachsener – Pandemiebedingte Herausforderungen und theoretische Perspektiven

ZusammenfassungDas Feld der Alphabetisierung und Grundbildung Erwachsener nimmt einen zentralen Stellenwert in der Förderung gesellschaftlicher Partizipation von vulnerablen Gruppen ein. Die Covid-19 Pandemie bringt neue Transformationsprozesse mit sich, die sich auch auf die Risikofaktoren von Drop-out (Abbruch) in der Alphabetisierung und Grundbildung auswirken. Drop-out kann als Verstärker sozialer Ungleichheit beforscht werden, was insbesondere im Kontext pandemiebedingter Lernbedingungen an neuer Relevanz gewinnt. So werden in diesem Beitrag die pandemiebedingten Herausforderungen für das Feld mit besonderer Berücksichtigung von Drop-out herausgearbeitet. Anhand eines mehrstufigen Analyseverfahrens (Expert:inneninterviews & narratives Review) kann gezeigt werden, dass „Medienkompetenz und Medienzugang“, „Kontinuität und Kursstruktur“ sowie „Vertrauen und Kursbindung“ zwar als bereits bekannte Risikofaktoren für Drop-out gelten, jedoch vor dem Hintergrund pandemiebedingt veränderter Strukturen eine gestiegene Bedeutung bekommen. Pandemiebedingte Transformationsprozesse wirken wie ein Brennglas auf bereits bestehende Problemlagen im Feld der Alphabetisierung und Grundbildung. Somit kommt der pädagogischen Aufgabe der entsprechenden Gestaltung von unvorhersehbaren Veränderungen eine besondere Bedeutung zu, um der Verschärfung sozialer Ungleichheiten entgegenzuwirken.

Social Media Engagement Anxiety: Triggers in News Agenda

In this study, we focus on decrease in public activity of news media audiences on social networks and on possible media strategies of “comfortable involvement” of socially anxious people into commenting. We draw upon cognitive research that describes a vicious circle of user experience. The environment for the anxiety scenarios in relation to user comments creates favorable conditions for the growth of digital escapism. We hypothesize that: 1) Users who are looking for getting rid of anxiety on social networks are drawn into an even greater spiral of anxiety, interacting with other people in the context of news stories that provoke the whipping up of negative emotions;2) The dynamics of the cascade of messages can depend on the characteristics of the emotions embedded in the message and its context. We took 10 Russian regional media and their content on the VKontakte social network published from November 2020 to November 2021 and processed all commented posts in each media. Frequency analysis of messages demonstrated no specific pattern for anxiety. Number of comments and likes on comments correlated with specific regional issues and local news agenda more than with coronavirus agenda. Our results showed that engaged users comment more than 2 times in a period but drop out of the discussion in 2–3 moths if agenda is not escalated by news outlet itself. Emotional triggers in news stories containing reasons for anxiety depends on a combination of factors from the site’s functionality to the type of news media and local information policy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The virtual teaching process and academic self-efficacy in learning and the intention to drop out university studies in times of Covid-19

The purpose of this study was to test the relationship between academic self-efficacy, learning self-efficacy, the teaching-learning process, student-teacher interaction, mediated by learning self-efficacy and the level of influence on students' intention to drop out. university students in times ofCovid-19. The study comprises two phases, firstly, an exploratory factor analysis using the IBM-SPSS, to determine the correct adaptation of the factors with their corresponding items, secondly, the confirmatory analysis was carried out, for the validation of the structural model proposed and establish the relationships of the factors proposed in the model, the PLS-SEM methodology, Modeling of Structural Equations with Partial Least Squares, was used. The model was validated with a sample of 230 students in a private university in Peru and is explained by the determination coefficient R2 in 51.9%, so it is inferred that there are other latent factors or variables that would explain the intention dropout in university students by 48.1%. © 2021 IEEE.

Effects of COVID-19 on individuals in Opioid Addiction Recovery

Opioid Use Disorder (OUD) is one of the most severe health care problems in the USA. People addicted to opioids need various treatments, including Medication-Assisted Treatment (MAT), proper counseling, and behavioral therapies. However, during the peak time of the COVID-19 pandemic, the supply of emergency medications was disrupted seriously. Patients faced severe medical care scarcity since many pharmaceutical companies, drugstores, and local pharmacies were closed. Import-export was also canceled to consent to the government emergency law, i.e., lockdown, quarantine, and isolation. These circumstances and their negative effects on OUD patient's psychology could have led them to a drop out of MAT medications and persuaded to resume illicit opioid use. This project involves collecting and analyzing a large volume of Twitter data related to MAT medications for OUD patients. We discover the Active MAT Medicine Users (AMMUs) on twitter. For this, we build a seed dictionary of words related to OUD and MAT and apply association rules to expand it. Further, AMMUs' tweet posts are studied 'before the pandemic' (BP) and 'during the pandemic' (DP) to understand how the drug behaviors and habits have changed due to COVID-19. We also perform sentiment analysis on Tweets to determine the impact of the COVID-19 pandemic on the psychology of AMMUs. Our analysis shows that the use of MAT medications has decreased around 30.54%, where the use of illicit drugs and other prescription opioids increased 18.06% and 12.12%, respectively, based on AMMUs' tweets posted during the lockdown compared with before the lockdown statistics. The COVID-19 pandemic and lockdown may result in the resumption of illegal and prescription opioid abuse by OUD patients. Necessary steps and precautions should be taken by health care providers to ensure the emergency supply of medicines and also psychological support and thus prevent patients from illicit opioid use. © 2021 IEEE.

In Patients with Obesity, Are Affective Temperaments Associated with Attrition? An Evaluation during and before the SARS-CoV-2 Pandemic.

Timely data on attrition from weight loss programs for patients with obesity during the SARS-CoV-2 pandemic are lacking, so we aimed to contribute to filling this gap in the literature by comparing attrition during or outside of the SARS-CoV-2 pandemic and its possible association with patients' affective temperaments, psychopathology, and clinical variables. Two-hundred and eleven outpatients with obesity were recruited and completed the Temperament Evaluation of Memphis, Pisa, and San Diego Auto-questionnaire, Binge Eating Scale, Beck Depression Inventory, and State-Trait Anxiety Inventory. Those who dropped out during the pandemic period were mostly men, with younger age of weight gain, and with a larger waist circumference than completers. Patients with obesity who dropped out outside of the SARS-CoV-2 pandemic showed marked levels of depression, anxiety, binge eating episodes, and higher affective temperaments (but the hyperthymic one) when compared to their counterparts. The cyclothymic temperament slightly increased attrition (OR = 1.13, 95% CI 1.00-1.27 p = 0.05) outside the pandemic, while during the pandemic, male gender (OR = 3.50, 1.04-11.7, p = 0.04) was associated with attrition. These findings suggested that male patients with obesity are at particular risk of drop-out from weight-loss treatment during the SARS-CoV-2 pandemic; contrariwise, outside the pandemic, affective temperaments could be a useful baseline assessment for defining the attrition risk in these patients.

The early impact of the COVID-19 pandemic on mathematical competencies on entry into a UK university

Students from England and Wales had their A-level results in 2020 decided by a 'triple lock' system as traditional examinations were cancelled due to the pandemic. Therefore, there was a fear that students were either being unfairly judged (Arden University 2020) or would enter university with a reduced understanding of concepts leading to an increase in drop-out rates (Staton, 2020). To measure mathematical ability, diagnostic testing is used at universities. This is now a well-established practice that supports students and their lecturers in discovering areas of mathematical strength and weakness upon entry to a course (Lawson, 2003;Hodds, Shao, and Lawson, 2020). This article compares the entry competencies of students arriving at one UK university in October 2020 with those who entered in previous years, using the same diagnostic test taken by all students as the method for comparison. Students who received their A levels in the year of entry are also compared to those who entered with A levels from years prior, allowing for a fair comparison of students in 2020 in particular. Furthermore, the abilities of students at different A-level grades are also compared. The results showed that students in 2020 appear not to be disadvantaged by the issues caused by the pandemic. On the contrary, many students outperformed colleagues who had summative assessment prior to 2020. © 2021 The Author(s) 2021. Published by Oxford University Press on behalf of The Institute of Mathematics and its Applications. All rights reserved

Rural Population and COVID-19: A Model for Assessing the Economic Effects of Drop-Out in Higher Education

Higher education is one of the ways to overcome social inequalities in rural areas in developing countries. This has led states to develop public policies aimed at access, retention and timely graduation of students in those sectors, yet the high drop-out rates among the rural student population, which were catalysed by COVID-19, prevent the intrinsic and extrinsic benefits of obtaining a higher education degree from materialising. Thus, the study of the phenomenon of dropout before and after the pandemic has not sufficiently addressed the economic issues raised by this phenomenon for the different actors at the educational level. The purpose of this paper is to model the economic effects of rural student dropout at the higher education level for students and families, Higher Education Institutions (HEIs) and the State, based on public policies for access to higher education, in the pandemic and post-pandemic scenario. In order to delimit the operationalisation of the proposed model, a set of undergraduate training programmes in Colombia was taken as a reference. System dynamics was used as the main modelling technique. The model was based on data from the 20 training programmes with the highest number of students enrolled in rural areas for the year 2019, by running three computational simulations. The results showed the description of the dynamic model and the financial effects of dropout for the actors of the educational level with the current policies of access to higher education, the scenario in which COVID-19 would not have occurred and the consolidation of the public policy of tuition fee exemption in public HEIs as a result of the pandemic. It was concluded that the model developed is very useful for the valuation of these economic effects and for decision-making on policies to be implemented, given that the costs of dropout are characterised by high costs for students and their families as well as for HEIs, and where it was determined that current policies are inefficient in preventing and mitigating dropout. Copyright © 2021 Guzmán Rincón, Barragán Moreno and Cala-Vitery.

Hospital School Program: The Right to Education for Long-Term Care Children.

Education and health are two inseparable aspects of a single dynamic which aims to support and increase the physical and mental well-being of children and young people. Children must be guaranteed two rights: the right to study and the right to health. Schools capable of reconciling these two fundamental needs are represented by school in hospital and home schooling. Thanks to this flexible teaching method, it is possible to support the child and his or her family during hospitalization, and to prevent consequences such as school failure and dropout. Hospitalization is always a traumatic event for children, in which white coats are unknown figures, perceived all the more threatening the younger the child: a threat to one's integrity, loss of autonomy, distorted perception of time, loss of confidence, and a sense of abandonment. Therefore, it is important to create a communicative basis that facilitates the child's adaptation to the new hospital environment and establishes continuity during this period of time. Teachers play a significant role within the context of such difficulties. They need to understand patients' emotions and act as a bridge between the small inpatient room of the child and the outside world. In this article we examined: (1) the School in Hospital and the reasons why it is a valid resource for the psychophysical rehabilitation of the student in a hospital; (2) the role of the teacher in hospital and the difficult context in which the teacher has to work; and (3) how the school in hospital was challenged by the SARS-CoV2 pandemic.