The effects of working from home during the COVID-19 pandemic on work–life balance, work–family conflict and employee burnout
Bibliographic record
Abstract
Purpose Despite the extent of working from home (WFH) during the coronavirus disease 2019 (COVID-19) pandemic, research exploring its positive or negative effects is exceptionally scarce. Unlike the traditional positive view of WFH, the authors hypothesize that WFH during the COVID-19 pandemic has triggered work–life imbalance and work–family conflict (WFC) for employees. Furthermore, the authors suggest that work–life imbalance and WFC elicit burnout in employees. Design/methodology/approach Using a time-lagged design, the authors collected data in three waves during the peak of the first wave of the COVID-19 pandemic to test the authors' hypotheses. Findings Overall, the authors found good support for the proposed hypotheses. WFH had a significant positive relationship with burnout. WFH was negatively related to work–life balance (WLB) and positively related to WFC. Both WLB and WFC mediated the effects of WFH on burnout. Practical implications This is one of the earliest studies to explore the harmful effects of involuntary WFH and identify the channels through which these effects are transmitted. The practical implications can help managers deal with the adverse effects of WFH during and after the COVID-19 crisis. Originality/value The authors' results significantly contribute to the research on WFH and burnout and present important implications for practice and future research.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".