Exploring layers of context-related work-from-home demands during COVID-19
Bibliographic record
Abstract
Purpose This article identifies the contextual demands impacting the work from home (WFH) experience during the COVID-19 pandemic and considers their respective impact on employees' personal and work-related outcomes. Design/methodology/approach Drawing on a qualitative methodology, the authors thematically analyzed written narratives from 41 employees who had been required to work from home during the COVID-19 pandemic. Findings Data analysis identified four layers of contextual demands (omnibus, task, social and personal) that had resulted in participants being required to work from home. Drawing on this finding, the authors develop a conceptual process model to extend current theory and explain how the respective demands impact individual affective reactions and work-related experiences, health and wellbeing. The authors’ findings offer new insights into contexts where WFH becomes mandatory, indicating that it is characterized by a range of challenges and opportunities. Practical implications This paper signals the need to provide employees with a realistic preview of working from home demands, including interventions to better prepare them to navigate the daily stressors of working from home; and provision of virtual employee assistance programs in the form of online counseling. Originality/value This paper explores a unique phenomenon – the mandatory requirement to work from home during the COVID-19 pandemic and its impact on employees' personal and work-related experiences and outcomes.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".