Human Sensorial Exploration in Designing a Comfortable Patient Room
Bibliographic record
Abstract
Some researchers have addressed what home means in architectural terms to human beings, and some others have investigated comfort and homeyness in relation to patients. Also, many studies, and on top of them Pallasmaa’s theory about the architecture of senses, have shown the importance of considering all human five senses in the design of a built environment and its positive effect on people’s sense of well-being. However, there has been no scientific evidence supporting the relationship between human senses and designing homely healthcare spaces which can result in the development of healing environments. So, this research investigates the comfort and homely design factors for patient rooms and their relationship to the five human senses in order to create multisensorial experiences. Also, this project aims to raise awareness among healthcare designers, caregivers, and patients in terms of the importance and role of considering all human senses in order to create comfortable and homely atmospheres in hospital rooms. \nThe current study is analytical and theoretical using qualitative research. Discourse analysis is conducted to find and evaluate the design recommendations based on sensorial qualities. Through the research-creation process, the current images of a cancer ward room in Montreal are analyzed. The final creation of this project is a webpage to reveal sensorial design recommendations as an information mechanism. The research-creation project has a didactic approach and tries to teach different design recommendations for a patient room and their sensorial aspects by interacting with the analyzed hospital room images using a website.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".