Exploring the facilitators and barriers to shopping mall use by persons with disabilities and strategies for improvements
Bibliographic record
Abstract
Persons with disabilities face challenges which impact on their ability to accomplish daily activities such as moving around, communicating and fulfilling social roles. Social participation assumes individuals with disabilities live within their community and interact with others. Shopping malls are public spaces used by individuals for various reasons. Here, all components of the social and physical environment interact and have an impact on social participation. This exploratory and qualitative study provides a multi-perspective assessment of the usability, as well as of the environmental facilitators and obstacles to social participation in shopping malls. The results also suggest necessary improvements. We interviewed 15 persons with disabilities, 15 rehabilitation professionals and 9 shopkeepers. Participants viewed the mall as a multifunctional place for everyday use, but at times, also as a limiting place. Multiple facilitators and obstacles were identified; the most important were interaction with shopkeepers and the mall’s design for mobility or wayfinding. All participants agreed shopkeeper training and an improved awareness of the needs of persons with disabilities would be beneficial. Multiple stakeholders’ perceptions provide a basis for further investigation about needed changes and their potential for making malls more welcoming and inclusive to all.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".