Indoor environmental quality and employees’ workplace satisfaction: a case study of university buildings
Bibliographic record
Abstract
Abstract Indoor environmental quality (IEQ) is an effective factor in evaluating the performance of employees in the workplace. This paper aims to investigate the IEQ of an office building of Imam Khomeini International University (IKIU), by evaluating the relationship between staffs' satisfaction and the orientation, window to wall ratio (WWR), and their gender. The results indicated that the size and landscape of the rooms, WWR, place of worktables, cooling and heating facilities and lighting systems, upgrading partitions and adding new spaces without increasing systems capacity, and the shared space usage by multi-users are the key factors that impact users’ satisfaction. Moreover, user comfort did not only depend on the features and equipment of the building and physical and physiological factors, but also on the habits, culture, and expectations of individuals. The results showed the same thermal satisfaction for both genders in the warm season and slightly higher dissatisfaction of females (4.62% higher compared to men) in the cold season. In addition, the main sources of noise were from the doors and the students passing the hallways. In conclusion, improving indoor air quality and thermal comfort were the most important ways to improve users' performance. This study is the first research concentrated on evaluating the current status of offices and presenting solutions to improve the IEQ factors in order to improve IKIU employees’ performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".