Workers' well-being and job performance in the context of COVID-19: a sector-specific approach
Bibliographic record
Abstract
Purpose The main objective of this study is to scrutinize the relationship between workers' well-being and job performance across sectors during the first lockdown. The authors also aim to examine the indirect effects of satisfaction with work-life balance, reopening of schools after closure, workload and teleworking on performance through well-being. Design/methodology/approach The authors used a sample of 447 Canadian workers collected online during the first lockdown to perform a series of structural equation models. Findings The results show that workers' well-being increases job performance and satisfaction with work-life balance has a positive indirect effect on job performance through well-being in all sectors. This finding suggests that workers' well-being mediates the relationship between satisfaction with work-life balance and performance. However, the reopening of schools, increased workload and teleworking do not have universal effects across sectors. Practical implications All organizations should implement human resources (HR) practices that promote workers' well-being and family-friendly workplaces, especially during the pandemic. Conversely, teleworking has a sector-specific effect that must be considered when implemented. Originality/value This study stands out by strengthening the bridge between workers' well-being and job performance. The effects of well-being and satisfaction with work-life balance on job performance are universal, while the impact of reopening of schools, increased workload and teleworking are sector-specific.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".