Perceptions of IEQ, well-being and work performance in work-from-home settings
Bibliographic record
Abstract
In the final week of March 2020, approximately 4.7 million employees in Canada shifted to a work- from-home (WFH) arrangement in response to the pandemic. A similar transition occurred on a global scale. With the growing trend of remote work and the shift towards home offices, understanding the effects of objective and subjective indoor environmental quality (IEQ) on individuals' well-being and productivity is crucial. Much of the existing research has been conducted in traditional office environments rather than in WFH settings. This paper aims to provide an analysis of the subjective evaluation of IEQ conditions within WFH settings and their perceived influence on both work performance and well-being. The analysis is based on a field study conducted in the summer of 2022, which involved 94 participants. The most prevalent features available in the workspaces of these individuals included access to exterior views, operable windows, ample daylight, and sufficient workspace. Notably, these features not only received the highest satisfaction ratings from the participants but also appeared to exert a positive impact on both work performance and well-being. Conversely, the most frequently encountered challenges by WFH employees were associated with disturbances originating from street noise and family members, as well as unwanted interruptions. These issues were found to have a more pronounced effect on workers' well-being compared to their impact on work performance. Furthermore, the study revealed significant correlations between overall workspace satisfaction and performance, as well as between well-being and performance, underscoring the interconnectedness of these factors in the WFH context.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".