Hospital design for inpatient psychiatry: A realistic umbrella review
Bibliographic record
Abstract
The evaluation of the effects of architecture and design in psychiatric hospitals primarily focuses on final outcomes, such as disease progression, and is made from the perspective of evidence-based medicine. Meanwhile, the evidence-informed, realist approach addresses how the intervention works. Understanding the underlying action mechanisms of the intervention is needed to facilitate its scaling-up and adaptation in new environments. This umbrella review reports in which ways architecture and design have an effect on patients' and staff experience in inpatient psychiatric hospital. The search was constructed around three key concepts (psychiatric hospital, design, and staff and patient outcomes) and was conducted across three reference databases (Embase, Medline, and PsychINFO). Academic and gray literature was analyzed. Information on design and architectural features in psychiatric hospitals, their effects on patients and staff experience, and the acting mechanisms enabling these effects were extracted. From 951 original references, 14 full texts were included in the analysis. Design and architectural features (e.g., aesthetic appeal of places, home-like environment) in psychiatric hospitals address patients' stress, boost social interaction, foster patients' autonomy and feelings of control, ensure respect for patient's privacy and dignity, and prevent under-and overstimulation. Using theory-driven evaluation may facilitate future hospital renovation and the evaluation of its effect.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".