A Sensory Approach to Design: Inclusive Principles
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to address the importance of sensory input within the built environment and develop design guidelines to accommodate the needs of all users. Typically, people receive information about the surrounding environment through their senses collectively. However, sensory processing problems may occur when sensory signals do not integrate to provide appropriate responses. As a result, the environment may cause a person to feel confused or irritated. METHODS: A mixed methods approach was utilized to gather data including 1) focus group, 2) interviews, 3) observations, and 4) surveys. Over 600 subjects participated including education specialists (n = 546), adults with developmental disabilities (n = 58), and administrators and staff (n = 30). The target population was individuals who experienced difficulties with sensory processing, integration and modulation, caregivers, educators, and administrators who worked with the target population. RESULTS: The findings show that individuals with sensory processing disorder view their environment differently than the general population. The data gathered was analyzed and coded to reflect six sensory categories: sight, touch, hearing, taste, smell, and motion (includes proprioception and vestibular senses). Each of these themes were further evaluated to develop "Design Principles for Inclusive Environments." Auditory and Tactile sound triggers were found to be the most problematic sensory responses and are the main focus of this manuscript. Unexpected sounds, background noise, and noise from mechanical systems were among the most problematic, while incorporating music and nature sounds were found to alleviate sound triggers. Tactile sensitivity in the environment was increased or reduced based on textures and materials, available personal space, and temperature. CONCLUSION: The research also showed that all users of a space benefited from the integration of inclusive design principles. This information is communicated in an easy-to-understand format that might benefit design professionals, educators, administrators, parents and the general public.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".