Evidence-Based Design Features Improve Sleep Quality Among Psychiatric Inpatients
Bibliographic record
Abstract
OBJECTIVE: The primary aim of the present study was to compare sleep characteristics pre- and post-move into a state-of-the-art mental health facility, which offered private sleeping quarters. BACKGROUND: Significant evidence points toward sleep disruption among psychiatric inpatients. It is unclear, however, how environmental factors (e.g., dorm-style rooms) impact sleep quality in this population. METHODS: To assess sleep quality, a novel objective technology, actigraphy, was used before and after a facility move. Subjective daily interviews were also administered, along with the Horne-Ostberg Morningness-Eveningness Questionnaire and the Pittsburgh Sleep Quality Index. RESULTS: Actigraphy revealed significant improvements in objective sleep quality following the facility move. Interestingly, subjective report of sleep quality did not correlate with the objective measures. Circadian sleep type appeared to play a role in influencing subjective attitudes toward sleep quality. CONCLUSIONS: Built environment has a significant effect on the sleep quality of psychiatric inpatients. Given well-documented disruptions in sleep quality present among psychiatric patients undergoing hospitalization, design elements like single patient bedrooms are highly desirable.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".