Territories of Engagement in the Design of Ecohumanist Healthcare Environments
Bibliographic record
Abstract
BACKGROUND: Increasingly, architectural and allied designers, engineers, and healthcare facility administrators are being challenged to demonstrate success in adroitly identifying and contextualizing ever-shifting and expanding spheres of knowledge with respect to the role of energy conservation and carbon neutrality in healthcare treatment environments and their immediate exterior environs. AIM: This calls for making sense of an unprecedented volume of information on building energy usage and interdigitizing complex and at times contradictory goals with the daily requirements of building occupants. Ecohumanist Design Strategies: In response, a multidimensional framework is put forth with the aim of advancing theory and practice in the realm of designers', direct caregivers', and administrators' engagement with ecohumanist design strategies in the creation of ecohumanist healthcare environments. CONCLUSIONS: Ten territories for engagement are presented that both individually and collectively express salient themes and streams of inquiry in theory and practice, within an operative framework placing the patient, the patient's significant others, and the caregiver at the center of the relationship between the built environment and occupant well-being.
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 | 0.027 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| 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".