Retail Design and the Visually Impaired: A Needs Assessment
Bibliographic record
Abstract
This study represents a first attempt to investigate the need for universal retail design in Canada. Specifically, the research objectives were to expand understanding of the unique challenge of visual impairment and the shopping experience of visually impaired consumers, and to identify gaps in retail design in order to better serve the visually impaired community. The researchers conducted three focus group interviews with a total of 17 informants recruited by an independent consultant who was affiliated with a visually impaired advocacy organization in the Greater Toronto Area in Ontario, Canada. Data were transcribed and then analyzed using QSR NVivo 8. Findings suggest that mobility is the biggest daily challenge facing visually impaired consumers. Retail shopping involves significant effort at every step of the process for visually impaired shoppers, including getting into the store; judging product quality; distinguishing colour; reading labels, store signage, and receipts; negotiating store layout and fitting rooms; dealing with store lighting; and interacting with sales associates. This paper identifies visually impaired shoppers’ needs for universal retail design, discusses implications, and makes recommendations to policy makers and industry practitioners in the defined fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".