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Record W4410289862 · doi:10.2196/73380

Perspectives From Canadian People With Visual Impairments in Everyday Environments Outside the Home: Qualitative Insights for Assistive Technology Development

2025· article· en· W4410289862 on OpenAlexaffvenueabout
Prajjol Raj Puri, Andréanne Coutaller, Frédérique Gwade, Soutongnoma Safiata Kabore, Joseph Paul Nemargut

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2025
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsSanté MontérégieAssociation for Canadian StudiesCentre de réadaptation Lethbridge-Layton-MackayCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsPreprintAssistive technologyVisual impairmentEveryday lifePsychologySociologyArchitectural engineeringHuman–computer interactionEngineeringComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Despite the abundance of assistive devices available, the accomplishment of many everyday tasks remains complex for people with visual impairments. While several studies have been conducted to identify the obstacles encountered when moving around outdoors, current knowledge is less abundant when it comes to the difficulties encountered in complex, indoor environments. Objective: This study aimed to identify the most important obstacles and facilitators encountered in everyday indoor travel environments outside the home for people with low vision and blindness. Methods: Data were collected from 20 participants with varying levels of vision from several cities across Canada in 2 web-based focus groups in both English and French. Using open-ended questions, participants shared obstacles and facilitators experienced or imagined during independent navigation in the following scenarios: coffee shop, hospital, big-box store, party with friends, and bus rides. Thematic analysis was conducted, and responses were either categorized as barriers or facilitators for each scenario. These were ranked by all participants via email according to their perceived importance in completing each scenario. Results: Across scenarios, the principal barriers to perceived success were inaccessible signage, difficulties walking around, problems finding a specific location, and unsuccessful interactions with others. The main facilitators across scenarios were helpful interactions with others, planning, accessible signage, and websites. The use of mobile apps was discussed but ranked as less important by participants. Though similar among the French and English groups, the rankings of the different facilitators and barriers were largely scenario-specific. The most barriers were mentioned in the coffee shop (n=8), followed by the department store (n=7) and bus or metro (n=7) for the English group, whereas the most barriers were in the department store (n=9), followed by the hospital or clinic (n=7) and coffee shop (n=6) for the French group. Conclusions: Though promising technologies have been developed to resolve some of the issues surrounding indoor navigation for people with visual impairments, they were not perceived as helpful as some other traditional methods of assistance, such as asking for help, by our participants. For the successful incorporation of indoor navigation technologies, it is important to understand how they integrate into the experience of people as they move in these dynamic environments. The successful use of technology is only possible if the physical environment permits and facilitates independent navigation.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.482

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0340.013
Scholarly communication0.0090.003
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.014
GPT teacher head0.316
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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