Principles for the Sustainable Design of Hospital Buildings
Bibliographic record
Abstract
Technological progress has had negative effects as well as positive effects if it affects the life industry and the entire ecosystem significantly through the great consumption of natural resources, and here the construction sector in general and the health sector, in particular, have a role in this. From the perspective of keeping pace with technological development, responding to environmental changes, and paying attention to hospital environments (especially since the emergence of modern epidemics), and because the construction sector is the largest consumer of energy in the world, which made international organizations move towards creating a sustainable environment in the construction of hospital buildings by reducing energy consumption. This research focused on studying the components and principles of sustainable design for hospital buildings and the environmental, economic, health, and social benefits of sustainable development in the healthcare industry. In addition to the research objective, which is to build a model as a guide to guide health care officials interested in applying sustainable design principles in hospital design, to achieve an ideal sustainable hospital environment. To achieve this goal, a comprehensive theoretical framework was built by adopting a descriptive and analytical approach and extracting the most important vocabulary and effective indicators for sustainable design in hospitals.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".