Mobile Applications for Participation at the Shopping Mall: Content Analysis and Usability for Persons with Physical Disabilities and Communication or Cognitive Limitations
Bibliographic record
Abstract
The aim of this exploratory study was to determine the important features in content and usability of existing mobile applications evaluating environmental barriers and facilitators (EBF) to participation for persons with physical disabilities presenting mild communication or cognitive limitations. A rigorous process based on a user-centered design approach led to the identification of two relevant mobile applications to evaluate the EBF. An accessibility expert, the research team as well as five users then tested the mobile applications in a shopping mall. A thematic content analysis of the research team's and users' comments established 10 categories of key features that adequately respond to the needs of the clientele targeted in this study. In terms of content, granularity and contextualization of the information provided were considered important. With respect to usability, relevant features were place finding, rating system, presentation of results, compatibility, user-friendliness, aesthetics, credibility of the information as well as connectivity/interactiveness. The research team and the users agreed on some aspects such as aesthetics, but had different perspectives on features such as the rating system or the connectivity/interactiveness of the application. The users proposed new features suggesting that the existing mobile applications did not correspond to all their needs.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".