Role of Contextual Factors in the Usability of Access Solutions for People With Disabilities
Bibliographic record
Abstract
An access solution consists of an access pathway, the channel that translates the functional intention of an individual with disability into a functional activity, and an access technology, which processes the physical or physiological data acquired through the access pathway. Recommendation of the appropriate access pathway depends on the nature and severity of the impairment, and the strength, reliability and endurance of client’s potential access sites. An important factor affecting the usability of access solutions is the context in which the client exploits it. Context, or contextual factors as it is referred to by the World Health Organization’s International Classification of Functioning, disability and Health (ICF), not only encompasses the client’s personal features and characteristics, it also includes environmental factors such as the milieu and time of access solution usage. A drawback of access strategies developed to date is that they do not account for personal and environmental factors and thus their usability declines when applied in more than one environment or by different users. In this paper we highlight the need for designing context aware access strategies, and the ways consideration of contextual factors can enhance the usability of access solutions for the population with severe and multiple disabilities. We also discuss how monitoring particular contextual factors can lead to creation of new access solutions .
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".