A state-of-the-art, systematic review of indoor environmental quality studies in work-from-home settings
Bibliographic record
Abstract
The COVID-19 pandemic has led to a significant increase in working from home worldwide, making the work-from-home (WFH) setting a crucial context for studying the influence of indoor environmental quality (IEQ) on workers’ well-being and productivity. A narrative and visual synthesis of 41 research articles on IEQ in WFH settings was conducted to identify the IEQ factors being measured and their correlations with perceived productivity and well-being. This review shows that the IEQ conditions at home were mainly within the recommended international standards. However, some high maxima were recorded, particularly for metrics related to quality of indoor air partly due to wider availability of evidence, which raised concerns regarding the suitability of indoor conditions while working from home. Despite the presence of these high maxima, workers generally rated all environmental factors highly. This could possibly reflect their lack of awareness of changes in environmental conditions, suggesting that monitoring environmental conditions might be necessary when working from home. Compared with traditional offices, workers seemed to be more satisfied with the environmental conditions at home although some WFH settings were found to be deficient in sound insulation, ergonomic and technological support, leading to multiple health complaints. Several studies have also demonstrated significant correlations between assessments of IEQ and those of productivity, physical and mental well-being. Future IEQ studies in WFH settings should consider using a longitudinal study design and including more representative samples, different seasons, multi-domain analyses, and multicountry and multicultural settings.
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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 | 0.015 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".