Research on Telework Changes Employees’ Stress Levels during the COVID-19 Pandemic - Specific Potential Stressors are Influencing the General Well-being of Employees
Bibliographic record
Abstract
Due to the expansion of COVID-19, a growing number of employees are forced to use telework as a new strategy to cope with the problem of continuing work during this sudden and serious pandemic.Though the research was to investigate the influences of telework on people's well-being in general, the question is under this special pandemic situation, how this sudden transition in the working environment will lead to changes in employees' stress levels specifically.Therefore, this article concentrated on the stress levels and aims to figure out how exactly telework influences employees' stress levels during the COVID-19 pandemic.Furthermore, this article also tries to shed light on whether corporations could consider telework as a work-mode choice for employees to better improve their working efficiency.To a global extent, meta-analysis is used in this article to assess the detailed evidence of the ways that the stress level of emplyees were influenced by telework during this pandemic.Three main factors were summarized that largely affect stress levels by combing 6171 participants.Results showed a positive correlation between telework and the stress level of employees during the COVID-19 pandemic.In addition, stress levels of employees during forced telework were mainly determined by work-life balance, work-home conflicts, and social interactions, which were significantly correlated with their working efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".