Workload, work–life interface, stress, job satisfaction and job performance: a job demand–resource model study during COVID-19
Bibliographic record
Abstract
Purpose This study, using a comprehensive job demand–resources (JD-R) model, aims to explore the pressures of workload, work–life interface and subsequent impacts on employee stress and job satisfaction, with implications for employee job performance, in the context of working from home during the COVID-19 pandemic. Design/methodology/approach A cross-sectional sample of employees at seven universities ( n = 4,497) and structural equation path analysis regression models are used for the analyses. Findings The results show that a partial mediation JD-R model was supported, where job demands (such as workload and actual hours worked) and job resources (including expectations, support and job security) have relationships with work interference with personal life and personal life interference with work. These have subsequent negative path relationships with stress. Further, stress is negatively related to job satisfaction, and job satisfaction is positively related to employee job performance. Practical implications Potential policy implications include mitigation approaches to addressing some of the negative impacts on workers and to enhance the positive outcomes. Timely adjustments to job demands and resources can aid in sustaining balance for workers in an uncertain and fluid environmental context. Originality/value This study makes a contribution to knowledge by capturing sentiments on working arrangements, perceived changes and associated outcomes during a key period within the COVID-19 pandemic while being one of the rare studies to focus on a comprehensive JD-R model and a unique context of highly educated workers' transition to working from home.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".