ARCHITECTURAL, INTERIOR DESIGN, AND TECHNOLOGICAL INTERVENTIONS TO FACILITATE REHABILITATION AND AGING IN PLACE
Bibliographic record
Abstract
Abstract Most older adults 60 years and above live in the community with either support from their caregivers or independently. Aging in place successfully involves safely carrying out activities of daily living at home without accidents and injuries. However, most traditional homes are not equipped with safe interior designs or support technological interventions, which could be a barrier to aging in place. We performed a narrative review to understand the importance of incorporating architectural, interior design, and technological interventions to support the rehabilitation of older adults with disabilities to successfully age in place. We reviewed 28 research papers from several research databases matching the inclusion criteria on interventions targeting older adults’ rehabilitation outcomes. We included studies that focused on various health conditions (e.g., spinal cord injury, stroke) and supporting home modification along with technological interventions. The review shows that the home environment has a significant impact on older adults’ overall health, and recovery, including functional capacities, cognitive processes, and emotional states providing autonomy, safety, and accessibility. Several safe design features were found, including barrier-free entrance, mobile stair climber, handrails and grab bars, anti-skid flooring, open layout, and shower seat. The integration of technologies (e.g., sensor lights, voice-activated devices) was identified as a promising avenue for enhancing the quality of life. Co-designing the built space with older adults was found to be effective. Our review highlights the need for collaboration between healthcare professionals, engineers, architects, and policymakers to design existing or new homes that support safe aging in place of older adults.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".