Managing Stress and its Consequences during Covid-19, the College of Technological Studies, Kuwait
Bibliographic record
Abstract
The unprecedented outbreak of Covid-19 and the suspension of classes while continuing teaching created disruption and a situation that added considerable stress not only to the management of technical and vocational institutions but also to teachers, trainers and students and their ability to cope with the situation. The shift to online teaching platforms rather than face-to-face learning caused emotional and physical consequences that affect the ability of teachers and trainers to achieve course objectives. This paper identifies and examines the emotional and physical consequences resulting from the use of online teaching platforms on teachers, trainers and students, in addition to examining the quality of online teaching platforms in achieving course objectives. The research involves designing, testing, and distributing questionnaires to a sample of teachers, trainers, and students as well as meeting with the Dean of the College of Technological Studies. The findings of this research revealed that teachers and trainers are more vulnerable to stress, and this can have a significant effect on teachers and trainers psychological and physical health and triggers emotional and physical consequences. In respect to students’ perception towards the effectiveness of applying online teaching platforms, the majority of students were disagreed that online teaching platforms helped in gaining the required skills, understanding cases studies and understanding the course topics. Thus, the management of the College of Technological Studies must ensure that teachers and trainers are well equipped with the required knowledge, skills and attitudes to overcome and/or reduce the consequences resulting from the use of online teaching platforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".