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Record W7039475703

The Nature of Healing: Living Architecture for Long Term Care & Rehabilitation Hospitals

2019· dissertation· en· W7039475703 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
Fundersnot available
KeywordsVisitor patternHealth careVulnerability (computing)Natural (archaeology)LimitingArchitectureLong-term careHealthcare systemRehabilitationPerception
DOInot available

Abstract

fetched live from OpenAlex

Healthcare interiors are perceived as stressful and isolating spaces; endured during times of vulnerability causing stress for patients, visitors and staff. This thesis examines studies, which prove that this psychological stress is intensified by the overly artificial and sterile conditions typical to medical environments. Further studies collected, reveal that this stress worsens the sensation of symptoms, causing increase in medication dosage and overall hinders the immune system and recovery outcomes. The paradox of the sterile healing environment is that nature, the adversary, is essential to healing processes. This thesis concentrates on research proving that not only do people generally prefer natural environments, as supported by the theory of Biophilia (see definition), but that exposure to elements of natural landscapes in healthcare spaces, greatly improves the holistic health of patients, visitors and staff. \nThis thesis examines the historical and contemporary factors influencing the design of hospitals. In the past few decades, healthcare design has progressed by integrating therapeutic design, through these strategies discussed, Evidence-Based Design and Biophilic Design (see definitions). However, through experience as a patient, visitor and designer in healthcare architecture, it is evident that there are still confines limiting the evolution of therapeutic design in hospitals. This thesis questions why healthcare standards prohibit the integration of living (plant) systems into more interior spaces, past the atrium. In seeking these answers it became clear that further innovation is necessary for architectural design to synthesize the qualities of sterile and therapeutic healing environments, to achieve healthcare homeostasis. \nVarious types of living systems are examined for exterior and interior application, including comparisons with artificial biophilic design strategies. The design intervention proposed in this thesis integrates living systems into typical architectural assemblies, and is referred to as Living Architecture. Living Architecture expands the threshold between healthcare interiors and horticultural therapy, to bring long-term plants closer to long-term patients. This is done by exploring the design possibilities for healthcare architecture to integrate spaces for patients to physically engage with living systems, year-round in various locations inside and outside the hospital. The challenge of this design study is meeting healthcare requirements for infection control, accessibility, maintenance and the financial limitations for public healthcare in Canada today. There is an opportunity to redefine health care architecture to suit the transformative nature of complex continuing care and rehabilitation hospitals. This progression could then influence other health care typologies to bring down the barriers between nature and medicine, by integrating living systems as the new standard approach to health care architecture.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.014
Scholarly communication0.0110.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.004
GPT teacher head0.207
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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