The State of Evidence-Based Design in Healthcare Interior Design Practice: A Study of Perceptions, Use, and Motivation
Bibliographic record
Abstract
This study addresses the design strategy known as evidence-based design (EBD), and seeks to discover the current state of EBD use and perceptions of United States and Canadian healthcare interior design practitioners. The study also addresses the motivations of healthcare interior designers to use EBD, as motivations may lead to further understanding of EBD's staying power as a strategy. Several emergent points of this nationwide survey of healthcare interior designers provide support for the findings of other EBD surveys administered to other related populations. These points include: ⢠Most responding healthcare interior designers engage with evidence-based design at an elementary level as determined by analysis using Hamilton's levels of EBD use (2009). ⢠Acceptable sources for evidence used to make design decisions vary, and some designers described that previous applied design practice experience (normative theory) is a valid source. ⢠EBD often assists practitioners in reaching a design decision, and most practitioners do not feel that EBD stifles their creativity. This study found that there is generally a high level of interest in EBD. Most practitioners understand the basic underlying principle of EBD (using credible research to reach the best possible design solution). The majority of designers reported that they used EBD for 50% or less of their design decisions on any given healthcare project. Further, designers mostly use EBD within the schematic design and design development stages of the design process. Designers' motivations for EBD use are both extrinsic and intrinsic in nature, and the majority of the participating designers believe that using EBD will improve their projects and also help sell their design solutions. Generally, results seem to confirm that EBD is likely in the early stages of making its mark on healthcare interior design. EBD has yet to reach widespread consensus in meaning and application, yet holds promise to provide enhanced validation to design processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".