Simulation-Based Mock-Up Evaluation of a Universal Operating Room
Bibliographic record
Abstract
Designing or renovating a physical environment for healthcare is a complex process and is critical for both the staff and the patients who rely on the environment to support and facilitate patient care. Conducting a simulation-based mock-up evaluation as part of the design process can enhance patient safety, staff efficiency, as well as user experience, and can yield financial returns. A large urban tertiary care center located in Vancouver, Canada followed a framework to evaluate the proposed design template for 28 universal operating rooms (ORs) included within the OR Renewal Project scope. Simulation scenarios were enacted by nursing staff, surgeons, anesthesiologists, residents, radiology techs, and anesthesia assistants. Video and debriefing data were used to conduct link analyses, as well as analyses of observed behaviors including congestions and bumps to generate recommendations for evidence-based design changes that were presented to the project team. Recommendations incorporated into the design included relocating doors, booms, equipment, and supplies, as well as reconfigurations to workstations. These recommendations were also incorporated into the mock-up and retested to iteratively develop and evaluate the design. Findings suggest that incorporating the recommended design changes resulted in better room utilization, decreased congestion, and enhanced access to equipment.
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 | 0.008 | 0.019 |
| 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) | 0.004 | 0.000 |
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.
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".