Nature Nurtures: Architectural Greenery Systems to Support Healing in Canadian Hospitals
Bibliographic record
Abstract
How can living plant systems be combined with healthcare facility architecture to increase beneficial interactions with nature, while still maintaining healthcare standards of safety, efficiency, and control? Nature can provide healing benefits to hospital occupants by lifting their spirits and by counteracting the difficulties of fighting illness. Architectural designers can help to create more positive hospital environments by utilizing vegetation as a building material and in building systems. Vertical and raised greenery systems such as living walls, green façades, and green roofs can deliver more accessible green spaces in dense, urban hospital sites. Greenery systems can also create synergistic relationships between plant life and functional healthcare programs. \nThis thesis analyzes the benefits, costs, and challenges of greenery system typologies and their various construction types. Demonstrated are architectural designs for key patient and visitor spaces in a hypothetical patient tower on an existing Canadian hospital redevelopment site. Within this design, greenery systems support long-term care patients of specialty units like rehabilitation, palliative care, acute elderly care, and mental health. By providing knowledge about the application of architectural greenery systems, this thesis promotes a sustainable design of greenery systems and a plant-based philosophy to the way hospitals are envisioned, and health care is achieved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".