Employee well-being in the COVID-19 pandemic: The moderating role of teleworking during the first lockdown in the province of Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a profound effect on all aspects of society, including mental health. Many employees have had to pivot suddenly to teleworking to prevent the virus from spreading. While teleworking may have some negative consequences, it may also represent a human resources practice that may improve employee well-being. OBJECTIVE: The study main objective was to determine if teleworking played a moderating role in the relation between potential stressors and employee well-being during the first months of the COVID-19 pandemic when working from home. This was based on the theory of conservation of resources. METHODS: Multivariate regression analyses were conducted with Stata 13 software to determine the contribution of potential stressors on employee well-being, as well as the moderating role of teleworking on a sample of 480 Canadian employees. Data were collected once for white and blue collar from both public (67.08%) and private (32.92%) business sectors. RESULTS: Results indicated that work-life imbalances, workload, and marital tension were associated with lower levels of well-being. On the other hand, teleworking and household income were associated with higher levels of well-being. Teleworking also moderated the differences in well-being between the public and private sectors. Teleworking in the public sector seems to increase employee well-being. Conversely, working on-site in the public sector seems to decrease well-being. CONCLUSIONS: Employers need to proactively address work-life imbalances, workloads, and teleworking to maintain employee well-being. Specific recommendations are offered to ensure that teleworking remains positive for employee well-being both during a pandemic and afterward.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".