Escaping the Asylum: Reimagining the Architecture of Psychiatric Care
Bibliographic record
Abstract
While most individuals with mental illness currently receive treatment in the community, dedicated psychiatric care facilities are critical spaces for those in crisis or exhibiting severe symptomology.For safe care, these facilities mandate a low risk environment and architectural design is critical in mitigating instances of patient violence, self-harm and exit-seeking.Yet what is necessary for high risk patients may be counter-therapeutic and disengaging for those seeking support.In psychiatric care, the architectural environment can enhance or undermine patient wellness, staff interactions, and even public perception.To contribute to redefining the mandate of architecture in this context, this thesis proposes a renovation of the Psychiatric Emergency Services (PES) units at both the Civic and General Campus of The Ottawa Hospital.These renovations are approached through a series of details that seek to prioritize patient well-being and facilitate therapeutic interactions while remaining safe and appropriate for those in psychiatric crisis.i To my advisor, Federica Goffi: Thank you for your guidance and constant support throughout the course of this past year.Your thoughtful insights, wealth of knowledge, and positivity have been instrumental to this thesis and your unique perspectives and fascinating ideas will have a lasting impact on how I understand and practice architecture.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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".