Building Better Healthcare Facilities through Evidence-Based Design: Breaking New Ground at Vancouver Island Health Authority
Bibliographic record
Abstract
Many of today's healthcare facilities were constructed at least 50 years ago, and a growing number have outlived their useful lives. Despite renovations and renewals, they often fall short of providing an appropriate care setting. Clinicians and staff develop a mixture of compromises and workarounds simply to make things function. Evidence-based design principles are often absent from new healthcare facilities, perhaps because of lack of awareness of the principles or because implementing them may fall foul of short-term and short-sighted budgetary decisions. In planning a new healthcare facility in 2008, the executive team at Vancouver Island Health Authority decided to adopt the evidence-based design approach. They conducted site visits to newly constructed hospitals across North America and beyond, to determine best practices in terms of design and construction. These engagements resulted in the implementation of 102 evidence-based design principles and attributes in Victoria's Royal Jubilee hospital, a 500-bed Patient Care Centre. This $350M project was completed on time and on budget, showing that using evidence need not result in delays or higher costs. To date, the results of the evidence-based design are promising, with accolades coming from patients, staff and clinical partners, and a number of immediate and practical benefits for patients, families and care teams alike.
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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 | 0.051 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".