Risk factors for dissatisfaction with the indoor environment in open-plan offices: an analysis of COPE field study data
Bibliographic record
Abstract
UNLABELLED: We applied binary logistic regression techniques to data collected from 779 participants in a field study of open-plan ('cubicle') offices conducted in nine buildings. Independent variables were physical conditions in the workplace, and dependent variables were derived from occupant satisfaction measures; personal characteristics were included as covariates. There was a significantly higher risk of dissatisfaction with privacy and acoustics (defined as being below the 20th percentile as opposed to being above the 80th percentile) associated with being in a small workstation, or being seated next to a window. A higher risk of dissatisfaction with ventilation was associated with being seated next to a window, temperatures substantially higher than the average neutral temperature, and a carbon dioxide concentration greater than 650 ppm. A higher risk of dissatisfaction with lighting was associated with panel heights greater than 66 inches (1.7 m), high reflected glare on computer screens, desktop illuminances outside 300-500 lux, desktop illuminance uniformity (min/max ratio) less than 0.5, and being in a workstation distant from a window. PRACTICAL IMPLICATIONS: We have demonstrated statistically significant relationships between indoor environment conditions in office spaces and environmental dissatisfaction risk. Although generally supported by prior research, not all of these risk factors are reflected in existing recommended practice documents for office design. Consideration of these findings in future revisions of such documents may be warranted.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".