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Record W4232421561 · doi:10.22215/etd/2015-11190

De-Medicalizing Architecture: Humanizing Design and Intervention for Elderly Care

2015· dissertation· en· W4232421561 on OpenAlexaboutno aff
Veronica Consales

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureBaby boomersHealth carePsychological interventionQuality (philosophy)Intervention (counseling)PopulationGerontologyPsychologyNursingMedicinePolitical scienceEnvironmental healthGeographyEconomics

Abstract

fetched live from OpenAlex

The role of architecture in the design of elderly centered health spaces is becoming increasingly more important as the baby boomer generation, now comprising nearly 23% of the Canadian population (Statistics Canada) approaches retirement. This group is advancing rapidly into old age, and will require facilities that are able to provide both individual and collective comfort and health. If this is accurate, we are led to ask how architecture can act as the environment and vehicle for providing quality care for the elderly population. This could also oppose the cure mentality central to conventional hospital design. The thesis will seek to answer: how can the design of humanized healthcare facilities for the elderly, focusing on quality architectural interventions that promote the notion of ‘care’, improve health indicators and promote community interaction. In other words: can architecture be de-medicalized.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.281
Teacher spread0.253 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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