COVID'S EFFECTS ON HIGHER EDUCATION:FROM CHALLENGES TO OPPORTUNITIES
Bibliographic record
Abstract
This research embraces medical and social issues that lead to changes particularly in the activities of higher education systems, focusing first of all, on the functioning of higher education institutions in the context of the COVID-19 Pandemic and the “quarantine” period. This research is designed to attract the attention of decision-makers and society in general to the importance of a focus on mental health in the higher-education system and to highlight these issues as a part of educational policy in the process of adapting to new living conditions during and after the quarantine period. As educators in different universities, we have noticed many shifts in energy and mood in ourselves and our students during the tsunami of changes brought on by COVID-19. As the way we deliver education pivots and pivots again, rather than just weather these changes, this research seeks to identify just what those changes are, how they are being addressed and the impact of them. The article analyses the reasons that brought the world community to actively study the social, psychological and mental consequences of the COVID-19 pandemic on students and staff of higher-education institution in general. Special attention in the article is given to the peculiarities of maintaining and saving the psychological health of students in conditions of chronic fatigue, overwork and ongoing stress. Based on the existing strategic` analysis and through the prism of the COVID-19 determinants, the main directions for changes in higher education institutions are proposed so as to preserve the mental health of people in the context of the COVID-19 pandemic, during the period of quarantine, and following.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".