Stroke Patients’ Experiences in an Adaptive Healing Room in a Stroke Rehabilitation Unit
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the user experiences (UX) of stroke patients residing in the adaptive healing room (AHR) and compared them to the UX of patients residing in standard private rooms (SPRs). BACKGROUND: Healing environments in healthcare settings can promote patients' healing processes, outcomes, and psychological well-being. The AHR was designed as a healing environment for stroke patients and has been previously evaluated in laboratory settings. This study was the first to evaluate it in its intended context-a stroke rehabilitation unit. METHODS: The UX of 10 patients residing in the AHR and 15 patients residing in SPRs were collected via structured interviews with a set of open-ended questions and analyzed using quantitative and qualitative methods. RESULTS: The AHR design features (orientation screen, skylight, and nature view) were rated positively by most patients. The skylight emerged as the least favorable. Responses to open-ended questions revealed that UX may be further improved if patients have more control over some of the settings (e.g., light intensity and nature views), and if the system allowed for more stimulation for patients at later stages of their recovery. Additionally, the results suggest that patients in the AHR have better UX than patients in the SPRs. CONCLUSION: The AHR has the potential to improve UX in the stroke rehabilitation unit. Patient feedback can be used to refine the AHR before carrying out clinical trials to assess the effect of the AHR on patient outcomes (e.g., sleep, mood, and length of stay) and stroke recovery.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".