Developing Innovative Solutions for Universal Design in Healthcare and Other Sectors
Bibliographic record
Abstract
For over half a century, researchers have sought to better understand the needs of people with disabilities in the built environment, and for more than a quarter century, they have sought to understand the effectiveness of universal design (UD) on a wide range of people and populations. This research led to the creation of the innovative solutions for Universal Design (isUD) building certification program, which addresses knowledge gaps in the practitioner's field with UD criteria. The isUD focuses on commercial buildings but aims to expand to other sectors including healthcare and residential settings. The research and outcomes used in the development and evaluation of the isUD combined with lessons learned from implementation of the isUD program suggest a path forward to improve and expand the program. Several research studies have evaluated the effectiveness of UD standards. One study compared university residence halls, one of which was built using a draft version of UD standards using a guided tour and online surveys among other methods.[1] Another study used online surveys to compare a workplace built using the isUD with the former workspace.[2] Another study used in-person surveys to compare public right-of-way features pre- and post- design intervention.[3] Lastly, an innovative doctoral dissertation that proposes a new methodological tool to evaluate UD in healthcare settings [4-5] has been analyzed to inform the isUD's expansion into the healthcare sector. The results indicate there is value in using UD to address equal access to and use of facilities for people with and without disabilities, and people of diverse social, cultural, and economic backgrounds. Facilities built using UD standards and tools are more usable, comfortable, and satisfying for users. However, the results also indicate there is room for improvement to make the isUD tool more effective. These improvements will better enable expansion of the tool to be usable in settings with more specialized requirements. While UD is often effective at improving human performance, health and wellness, and social participation across some measures, and while tools that assist with UD implementation may further help achieve these outcomes, to gain widespread adoption across multiple sectors, such tools must be shown to be consistently effective in achieving UD outcomes across all measures. These improvements can help expand availability of UD to a wider, more diverse audience.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".