Supportive Living Resident Suite Evaluation: Using Simulation to Evaluate a Mock-up
Bibliographic record
Abstract
To address the growing population of seniors, Alberta Health Services plans to provide 11,700 supportive living resident suites over the next decade. Before finalizing the design standards and guidelines for these facilities, a mock-up residential suite was constructed, outfitted with all necessary furnishings and equipment, and used to evaluate the proposed design. Evaluation of the physical space and accessibility for residents' and clinical tasks were accomplished using simulation, think aloud protocols and extensive debriefing sessions. Scenarios were created that focused on commonly-occurring tasks identified by frontline staff as being problematic for both residents and staff. These scenarios were then carried out by seniors and a variety of healthcare professionals to simulate expected room usage. Evaluation of the scenarios indicated that sufficient space was available in the design; however, areas prone to congestion were also identified. Recommendations to improve the design of the residential suite were made to improve accessibility (of cupboards, light switches and electrical outlets), storage (of power scooters/wheel chairs), and bathroom configuration (of emergency pull cord and grab bar locations).
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".