Exploring Healing Design Elements for Patient Room Design: Preferences of Adolescent Patients from Surgical Units
Bibliographic record
Abstract
Although the substantial influence of hospital environments on well-being has been widely recognized, research on the same topic for adolescent patients is limited. This study examined adolescent patients’ preferences in hospital room designs to identify design elements that can potentially promote the healing process. Eight computer-simulated patient room images were developed through the combinations of three design elements: trim style (straight vs. arch), ceiling and floor details (plain vs. decorated), and window view (nature vs. city). Adolescent patients evaluated the images of patient room images using seven preference evaluation words on a Likert scale. Adolescent patients did not differ in preference for either straight or arch trim styles (p > 0.05). Also, the different ceiling and floor details, such as plain vs. decorated, did not differ in the responses (p > 0.05). However, the study results indicated that more adolescent patients strongly prefer the nature view than the city view (p < 0.01), with higher peaceful, comfortable, pleasant, private, and enjoyable perceptions. Therefore, the window view was the most significant among the examined design elements, directing the value of relaxation and connection beyond the hospital environment. The results imply that 3D simulation of the patient room images adopting design elements can quantify adolescent patients’ perceptions of room design in conjunction with the Likert scale. Based on the results of this study, adolescent patient rooms should be designed and developed considering natural stimulation aspects in connection with the outside environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".