Home as an office: Investigating the associations between indoor environmental quality, well-being, and performance in work-from-home settings
Bibliographic record
Abstract
The COVID-19 pandemic has reshaped workplace dynamics, with work-from-home (WFH) becoming widespread, necessitating a deeper understanding of indoor environmental quality (IEQ) in residential settings. This study investigates the interplay of monitored IEQ conditions, perception-based assessments, and non-IEQ contextual factors on well-being and work performance in WFH settings. Ninety-five participants from Metro Vancouver, Vancouver Island, and the Seattle Metropolitan area provided data through objective IEQ monitoring and subjective questionnaires. Monitored parameters included t VOCs, PM 2.5 , CO 2 , temperature, humidity, and sound pressure levels, while subjective assessments captured perceptions of IEQ, overall workspace quality, and the impacts of working from home, alongside standardized measures of physical health, psychological well-being, and work performance. Results revealed weak associations between monitored IEQ conditions and well-being, and work performance outcomes, highlighting the limitations of seasonal, objective monitoring in capturing complex human-environment interactions. Conversely, perception-based assessments, such as satisfaction with ergonomic furniture, daylight, and workspace aesthetics, showed stronger associations with positive well-being and performance outcomes. Additionally, contextual factors, including work hours, gender, residence characteristics, and personality traits, were strongly associated with the outcomes, emphasizing the multifaceted nature of WFH experiences. This study underscores the importance of integrating subjective and objective methodologies to address the unique challenges of WFH 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".