Remote working and work performance during the COVID-19 pandemic: the role of remote work satisfaction, digital literacy, and cyberslacking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social distancing policies ushered in by the COVID-19 pandemic have altered working conditions and created new job demands. This study adopted the Job Demands−Resources (JD−R) model to investigate the relationship between demands and strains (i.e. social isolation, remote work stress, and fear of COVID-19) and remote work satisfaction and remote work performance. Additionally, the study sought to identify the moderating roles of employees’ digital literacy and cyberslacking in the relationship between remote work satisfaction and remote work performance. After analysing data collected from a sample of 340 Iranian remote workers, results showed social isolation, remote work stress, and fear of COVID-19 related to remote work satisfaction negatively and decrease remote work performance through the mediation of remote work satisfaction. Moreover, digital literacy and cyberslacking moderated the relationship between remote work satisfaction and remote work performance during the COVID-19 pandemic. By linking job demands and strains, psychological states, and employee output, this research notably contributes to the literature on remote working during the COVID-19 pandemic.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it