What's the ideal scooter? Stakeholders' perspectives on enhancing the usability and safety of motorized mobility scooters
Bibliographic record
Abstract
The use of motorized mobility scooters (MMSs) helps improve the quality of life of people living with disabilities by facilitating independence and community engagement. However, alongside these benefits, some challenges have been found to accompany their use. While issues that stem from the environment, the user, and the technology have been identified in literature as leading contributory factors to challenges with MMS use, technological problems have received much less attention. As the design of any technology plays a vital role in facilitating or impeding its own use, this study sought to understand diverse stakeholders’ perspectives on how technological factors influence MMS usability and safety, and how these can be enhanced. A qualitative descriptive method of inquiry was used in the study. A conceptual framework developed from the HAAT Model and the Compensatory frame of reference informed the data collection and analyses. Semi-structured in-depth interviews were conducted with a purposive maximum variation sample of 12 MMS users and 17 service providers who had experience with MMS-related services. The interviews were audio recorded and transcribed verbatim and content analysis was performed on the data. Analyses of the data resulted in three main themes. The first theme “Finding the right fit” explored the technology-related considerations and compromises made along the MMS procurement process; the second theme “Negotiating everyday challenges” explored the day-to-day challenges of MMS use that are associated with technological issues; and the third theme “Identifying solutions and barriers” explored ideas on enhancing MMS usability and existing or potential barriers. By investigating the technological issues that arise with MMS use in real world situations from the perspectives of diverse stakeholders, this study presents a unique point of view that has not been explored in literature. Findings from this study provide insights into how technological factors impact the usability and safety of MMSs for different user populations, during the performance of different activities, and its use in different environments. Stakeholders’ recommendations on enhancing MMS usability and safety can also help inform future innovation regarding MMS design.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".