The experiences of using an anti-collision power wheelchair for three long-term care home residents with mild cognitive impairment
Bibliographic record
Abstract
PURPOSE: Presented are three case analyses of long-term care home residents with cognitive impairment who tested an anti-collision power wheelchair. We discuss technology design and research implications for this population. METHOD: Case studies involved 371 h of participant observation and 7 h of open-ended interview with residents (n = 3), family members (n = 3) and clinical staff (n = 11). Thematic analysis generated themes related to technological, psychological and social aspects of residents' inclination and disinclination towards power mobility use. RESULTS: Themes examined the discordance between others' and residents' reports of anti-collision power wheelchair use; a facet of response bias; unanticipated implications for independence and dependence; and implications of device design for self-presentation. CONCLUSIONS: Technology alone is insufficient to help residents to fully benefit from the autonomy that a wheelchair intervention can provide: close attention is required to the social and organisational factors of institutional life. For technology to be acceptable, the design must meet the functional and aesthetic needs of users. Considerations in the design of future power wheelchairs for residents with cognitive impairment include capabilities to drive on uneven surfaces, effort-reducing driving modes, improved user interface usability, and acceptable driving speed, size and appearance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".