Bedside computer access for an individual with severe and multiple disabilities: A case study
Bibliographic record
Abstract
PURPOSE: This case study documents the process of designing a custom-tailored bedside computer access solution for a 20-year old individual with quadriplegia and reports the effects of computer access on her participation in life activities. METHOD: We adopted a person-focused approach to match the individual to an access solution. Two months after the access solution's introduction, we measured its impact using a 2-dimensional Fitt's Law test and questionnaire from the ISO 9241-9 standards document, typing tests, a usage log and a semi-structured interview. The Canadian Occupational Performance Measure was also administered pre- and post- access, focusing on the client's perceived ability to use the computer. RESULTS: After 2 months, the individual was spending an average of 8.4 h per day on the computer, engaging in electronic communication, recreational, and educational activities. She learned single-switch typing with a throughput of 1.03 bits/s and targeting accuracy of 87.5%. The questionnaire revealed that the client was thoroughly satisfied with the interface. These results were interpreted as positive gains in the International Classification of Functioning, Disability and Health domains of communication and social interaction. CONCLUSIONS: By addressing individual goals, abilities and relevant environmental factors, a bedside computer access solution can be developed for individuals in long-term care. The introduction of a computer access solution augmented the participant's communication, leisure and educational activities, as well as perceived independence.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".