Remote workers’ life quality and stress during COVID-19: a systematic review
Bibliographic record
Abstract
COVID-19 pandemic led to the adoption of a different working approach: "The remote working." Evidence about the association of remote working with stress outcomes and life quality is lacking. This systematic review provides an overview of the effects of COVID-19 pandemic on remote-workers' stress and life quality. We conducted systematic literature searches in databases including Pubmed, Scopus and Web of science, from September 2020 to September 2023. Screening of titles, abstracts, and full texts were performed according to the Preferred Reporting Item for Systematic Review and Meta-analyses. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. The review highlighted possible predictors (work-family conflict or a condition of social isolation) associated with improvement or worsening of quality of life and stress. The results highlighted the association between stress and family difficulties (β: -0.02, P-value <0.05), isolation during the first (β: -0.22, P-value <0.05) and second pandemic waves (β: -0.40, P-value <0.05) or due to the advancing age of workers (β:0.19, P-value <0.05) and (β: -0.05, P-value <0.05), furthermore some job categories presented greater stress such as teachers (16.94 ± 5.46). Conversely, remote working positively affected life quality, enhancing factors such as creativity (Average Variance Extracted, AVE: 0.41, R2: 0.17) and self-efficacy (AVE: 0.60, R2: 0.36). Future research should focus more on the relationship between work and family and on interventions that counteract social isolation.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".