An Exploration of Work-Life Wellness and Remote Work During and Beyond COVID-19
Bibliographic record
Abstract
Understanding work-life wellness contributes to improving the physical health, mental health, and productivity of remote workers. Due to physical distancing guidelines associated with the COVID-19 pandemic, many employees have been working from home, often without adequate training and resources. How has the work-life wellness of remote workers been impacted by this rapid transition to remote work, and how can work-life wellness be improved during and beyond these unprecedented times? Scholarly analyses about COVID-19 and remote work were reviewed, along with publications about remote work and work-life wellness. Literature indicates that the work-life wellness of remote workers could be influenced by lack of organizational supports during the transition to remote work, combined with COVID-19 related stress. Beyond the pandemic, organizations and employees will need support to find suitable remote work plans. Career development practitioners can assist clients by being aware of how the transition to remote work may be further complicated by home dynamics, COVID stress, overworking, and challenges collaborating. More research is needed to better support the new remote workforce, including choosing the most relevant construct to describe the relationship between work and life, understanding how COVID stress influences work-life wellness in the long term, and testing related training programs
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".