Computer-Related Assistive Technology: Satisfaction and Experiences Among Users With Disabilities
Bibliographic record
Abstract
Many people with disabilities use assistive technology devices (ATDs) for computer access. The specific focus of this exploratory study was (a) to assess the experiences, opinions, and satisfaction levels of 24 individuals with disabilities using computer-related ATDs; (b) to investigate their awareness of health risk factors related to computer usage; and (c) to examine the psychosocial impact of computer-related ATDs on users. Data were collected via telephone interviews with 24 individuals with physical disabilities who had experience using one or more ATDs. The Quebec User Evaluation with Assistive Technology instrument was used to evaluate users' satisfaction with ATDs in a number of dimensions, including their physical attributes. The Psychosocial Impact of Assistive Devices Scale measured the psychosocial impact (i.e., independence, competence, and adequacy) of an ATD on users. Additional questions were posed to gather information about user's opinions and experiences. Training appeared to be an important component for ATD users, many of whom preferred a setting to try out devices rather than group or individual training. Respondents with visual impairments revealed a higher level of adaptability versus those without visual impairments (p = .001). Additional research is needed to develop specific survey items focused on users of computer-related ATDs and the evaluation of the psychosocial impact of ATDs on computer users.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".