The Impact of the COVID-19 Pandemic and Transition to Distance Learning on University Faculty in the United States
Bibliographic record
Abstract
Background: The unexpected COVID-19 pandemic impacted not only students at institutions of higher learning, but also faculty who often made rapid transitions from face-to-face to online or distance learning. Distance learning has been shown to negatively impact college students’ physical activity, screen time, and mental health concerns. Little is known, however, about the transition and impact of the pandemic and distance learning on university faculty. Purpose: The purpose of this study was to examine the impact of the rapid transition from traditional face-to-face teaching methodologies to distance learning on professional quality of life, physical activity, screen time, and anxiety and depression among faculty. Methods: A descriptive survey design with snowball sampling, was used to collect data anonymously, online. An electronic survey was developed to explore professional quality of life, physical activity, screen time, anxiety, and depression. Results: The COVID-19 pandemic and subsequent rapid transition of teaching and learning methodologies impacted not only students, but faculty at institutions of higher learning. Most faculty indicated concerns with their professional quality of life, putting them at moderate risk of burnout. A negative correlation between leisure time and anxiety or depression was found as well as a positive correlation between increased screen time and depression. Conclusion: Increased screen time and decreased physical activity or leisure time may contribute to increased faculty burnout, depression, and anxiety. University administration may need to consider strategies to help faculty cope with transitions to unfamiliar teaching methodologies and self-care behavioral changes to avoid faculty dissatisfaction and disengagement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".