Modernized architecture may reduce coercion
Bibliographic record
Abstract
Introduction Prevention and treatment of aggression in psychiatric hospitals is achieved through appropriate medical treatment, professional skills, and optimized physical environment and architecture. Coercive measures are used as a last resort. In 2018 Aarhus University Hospital Psychiatry moved from 19th-century asylum buildings to a newly built modern psychiatric hospital. Advances within psychiatric care have rendered the old psychiatric asylum hospitals inadequate for modern treatment of mental disorders. Objectives To examine if relocating from a psychiatric hospital, dating from 19th century to a new, modern psychiatric hospital decreased the use of coercive measures. Methods This is a retrospective longitudinal study, with a follow-up from 2017 to 2019. We use two designs; 1) a pre-post analysis of the use of coercive measures at Aarhus University Hospital Psychiatry before and after the relocation and 2) a case-control analysis of Aarhus University Hospital Psychiatry and the other psychiatric hospitals in the Central Region. Data will be analyzed in STATA using an interrupted time-series analysis or similar method. Additionally case-mix and sensitivity analysis will be performed. Results Preliminary results show a 45% decrease in the total number of coercive measures and a 52% decrease in the use of mechanical restraint. The reduction that may reasonably be attributed to the relocation is still to be determined and will be presented at the congress. Conclusions The study may illuminate how future development and planning of psychiatric facilities might improve psychiatric treatment and increase the understanding of how structural changes might contribute the prevention of the use of coercive measures. Disclosure No significant relationships.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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".