Unprecedented Times: An Examination of Workers’ Experiences of Ill-Being and Well-Being during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic brought about unprecedented changes and challenges to workers’ work and home lives. The current study integrated insights from theories (e.g., the job demands-resources model, COR theory) and literature to investigate the contributors (e.g., job demands, job resources, job involvement, work engagement) to workers’ experiences of ill-being and well-being (i.e., work-life conflict, strain, burnout, job satisfaction). We tested our hypotheses with data from 693 full-time US and Canadian workers from MTurk, a sample of university faculty, a snowball sample, and a sample of veterinarians. We found that workers with greater job demands experienced greater ill-being (e.g., work-life conflict), and workers with greater job resources experienced greater well-being (e.g., job satisfaction). However, different types of job resources predicted different outcomes (e.g., general job resources predicted lower strain, but stimulating and supportive job resources did not). Further, we did not find evidence that job resources protected workers against job demands and strain or work-life conflict. Our results also suggest that stimulating job resources may have negative outcomes for workers, as they predicted exhaustion, through greater work engagement and work-life conflict. In addition, greater work engagement predicted both positive (i.e., job satisfaction) and negative outcomes for workers (i.e., work-life conflict, strain), supporting the notion that work engagement may be a double-edged sword. Finally, exploratory analyses revealed the job demands and job resources that are most salient and pertinent during the pandemic for workers. Implications for theory and practice are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".