Work engagement during the COVID-19 pandemic: insights from a cross-sectional web-survey with path modelling analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Workers are being highly impacted by the current COVID-19 pandemic. OBJECTIVE The present study aimed to identify the relationship between COVID-19 pandemic and work engagement among professionals who are working to offer living needs to people during the quarantine. METHODS A total of 364 private and state sector employees were recruited for this study. The subjects are divided into 159 women and 205 men with mean age 34.33 years old ± 11.40; and live and in Tunisia. Occupational category includes administrative employees (n = 101), employees in factories and companies (n = 137) and small businesses (n = 126). Participants were administered Work engagement scale (UWES), Work Domain Satisfaction Scale (WDSS), Satisfaction With Life Scale (SWLS), Promis Global Health Scale, Perceived Stress Scale (PSS4), and COVID-19 Fear scale. Partial Least Square modeling method was performed. RESULTS Results of the measurement model and the structural model confirmed the direct relationships between perceived stress, life satisfaction, and job satisfaction with Work engagement. Indirect links have also been highlighted between fear of CoviD-19 and overall health with engagement to work. CONCLUSIONS The established model can be used by researchers and management practitioners to act on constructs to increase engagement to work.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".