The Impact of the Design of Hospitals on Hospital Hoteling, Healing Process and Medical Tourism
Bibliographic record
Abstract
The design of hospitals is extremely important; any person may spend a part of his life in a hospital. Considering human interaction with the environment, psychological aspects of design and their impact on the moods of inpatients and their companions and medical team ergonomics and performance, the process of designing hospitals becomes more complex. The design of operational spaces in hospitals lack appropriate and functional properties in this regard. This paper considers the studies on the role of architecture and appropriate design on increasing the quality of the hospital hoteling, patients’ satisfaction, life expectancy and improved healing process, considering the design of spaces for inpatients’ companion and medical team. Furthermore, considering the potential and capacity of admitting foreign patients in Iran, the impact of these factors on medical tourism is also investigated. The objective of the present research is applied and is descriptive/analytical that considered various aspects of the problem from different perspectives. The findings of the research show that implementing the above standards in designing hospitals leads to increased satisfaction, improvement in inpatient morale, speedy healing process, and more relaxed patients’ family and medical staff. In addition, the impacts of the above items will result in increased medical tourism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".