Occupant Comfort and Satisfaction in Green Healthcare Environments: A Survey Study Focusing on Healthcare Staff
Bibliographic record
Abstract
Since the US Green Building Council introduced green building design strategies and measurement indicators as the name of LEED (Leadership in Energy and Environmental Design) in 2000, different rating systems for various types of facilities have been developed. LEED for Healthcare that was initiated to improve healthcare buildings’ energy efficiency and sustainability is one of them. Yet, there is still a strong debate over whether LEED certified hospitals provide more comfortable environments for the staff to work in than the counterparts. The purpose of this study was to identify effective factors influencing healthcare occupants’ comfort and satisfactions through comparing the perceptions of the healthcare staff from green hospitals with those from conventional hospitals. The study mainly targeted nursing staff because they spend about eight hours daily in such environment to improve patients’ health outcomes. By comparing the perceptions of the healthcare staff from green hospital (or LEED-certified hospitals) and conventional non-LEED-certified hospitals, the results from this study showed significant differences between two types of hospitals studied. This study additionally reviewed these effective elements, examined if they were indoor environmental quality elements or interior design elements, and discussed if green healthcare environments actually contributed toward improving occupant’s comfort and satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".