A Mobile App to Optimize Social Participation for Individuals with Physical Disabilities: Content Validation and Usability Testing
Bibliographic record
Abstract
Background: Social participation is beneficial for individuals’ health. However, people with disabilities that may lead to mobility limitations tend to experience lower levels of social participation. Information and communication technologies such as the OnRoule mobile application (app) can help promote social participation. Objectives: To obtain potential users’ perceptions on the usability and content of the OnRoule app for providing information on accessibility, as well as its potential to optimize social participation. Materials and Methods: Cross-sectional user-centered design study. Individuals with physical disabilities (n = 18) were recruited through community organizations and interviewed using a semi-structured guide. Interviews were recorded, transcribed, and analyzed using thematic analysis. Results: Three main themes were identified: (1) “user-friendliness”; (2) “balance between the amount and relevance of information”; and (3) “potential use of the app”. Discussion and Conclusion: Findings from this study indicated that the app was easy to use, had pertinent information, and enabled a positive experience of finding information. However, several areas of improvement were identified, such as the clarity of specific elements, organization and amount of information, optimization of features, and inclusiveness. Apps such as OnRoule could optimize social participation by facilitating the process of finding resources in the community and building a sense of connectedness between users.
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".