The Dynamics of Changes in the Personal Resources of Pedagogy Students During COVID-19 Pandemic
Bibliographic record
Abstract
Introduction : The COVID-19 pandemic that started in March 2020 had a considerable impact on the functioning of higher education and the psychological wellbeing condition of students. Research Aim : The aim of the presented research is to describe the dynamics of changes in selected personal resources: experienced stress, mental well-being, mental resilience and satisfaction with studies of students of pedagogy, in the period before and during the COVID-19 pandemic. Method : Research studies were conducted among 337 students of education in three time periods: in the first quarter of 2019–122 students, then in the first phase of the pandemic in June 2020 – 91 students and in June 2021 – 124 people. The following research tools were used: the PPS-10 scale to measure stress, the WEMBS scale to assess mental well-being, the RS-14 scale to determine the psychological resilience, and the Academic Satisfaction questionnaire. Results : More than half of the surveyed students experienced a high level of stress. In 2019–2021, mental well-being remained stable, mental resilience decreased, and satisfaction with studies increased. As stress increases, there is a decline in mental resilience and mental well-being. Stress and mental resilience are significant predictors of the mental well-being of students. Conclusion : Almost 60 percent of the surveyed students of pedagogy had personal resources that allowed them to adequately adapt to a pandemic situation. The ability to deal with stress in a pandemic crisis plays a key role.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".