'Thoughtful' Design and Healthcare- Comparative Case Studies of Medical Facilities built 146 years apart
Bibliographic record
Abstract
Health and well-being have a very direct relationship. The purpose of this research is to understand the impact of design of the healthcare facility to the recovery of those inside it. The most apparent differences between healthcare facilities built with human-centric approaches and those built with a broader or more ‘number-centric’ approach, is found when the exemplar facilities being compared, belong to different eras. For this research, the first facility chosen is one built during war- a promptly designed and promptly set-up hospital where patients were mostly nameless, faceless soldiers considered most important for numbers in the army- and the second, a hospital of the twenty-first century, one built involving residents, psychology, nature and aesthetics. This research compares the buildings on various architectural as well as general factors, including ideology, humanity of approach, design, materials, construction techniques, context and setting, aesthetics, socio-cultural parameters, morals and overall medical treatment merits. It concludes with an analysis of the similarities and differences of the two approaches, the changing requirements of a post-pandemic world, and what the latest definition of “future-ready” means for healthcare infrastructure.
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 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".