“Assistive technology in the home is choice and control… it’s freedom”: perspectives of people with physical disability using electronic assistive technologies in the home
Bibliographic record
Abstract
OBJECTIVE: People with physical disabilities can use electronic assistive technologies in their homes to increase their independence. These technologies range from disability specific environmental controls systems to mainstream smart home technologies and combinations of both. The purpose of this study was to explore the perspectives of persons with physical disabilities on their experiences using these technologies in their homes to inform future best practice. METHODS: This qualitative descriptive study used a World Café method underpinned by appreciative inquiry. Nine participants with spinal cord injuries, cerebral palsy or acquired brain injury participated in four World Café discussions. Inductive thematic analysis was used to analyse verbatim transcriptions. RESULTS: Five themes were identified: "Using Mainstream Technology", "Navigating Person-Technology Fit Amidst Change", "Making Technology Work in the Home", "Positive Impacts of Technology", and "Frustrations with Using Technology in the Home". These themes supported expected benefits and challenges. In addition, the opportunities provided by mainstream technology in terms of availability and affordability, funding frustrations and poor trust of suppliers were described. Making technology work required support as well as technological safeguards. IMPACT: Mainstream technology has improved and broadened possibilities for electronic assistive technology use in the home, which can be complex. It provides psychosocial benefits, but is also frustrating. Furthermore, using technology is a dynamic evolving journey as individual users must navigate changes in search of best person-technology fit. Successful use of technology requires support, as well as backup systems and safeguards to combat poor reliability.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".