Intelligent power wheelchair use in long-term care: potential users’ experiences and perceptions
Bibliographic record
Abstract
PURPOSE: Long-term care (LTC) residents with cognitive impairments frequently experience limited mobility and participation in preferred activities. Although a power wheelchair could mitigate some of these mobility and participation challenges, this technology is often not prescribed for this population due to safety concerns. An intelligent power wheelchair (IPW) system represents a potential intervention that could help to overcome these concerns. The purpose of this study was to explore a) how residents experienced an IPW that used three different modes of control and b) what perceived effect the IPW would have on their daily lives. MATERIALS AND METHODS: We interviewed 10 LTC residents with mild or moderate cognitive impairment twice, once before and once after testing the IPW. Interviews were conducted using a semi-structured interview guide, audio recorded and transcribed verbatim for thematic analyses. RESULTS: Our analyses identified three overarching themes: (1) the difference an IPW would make, (2) the potential impact of the IPW on others and (3) IPW-related concerns. CONCLUSIONS: Findings from this study confirm the need for and potential benefits of IPW use in LTC. Future studies will involve testing IPW improvements based on feedback and insights from this study. Implications for rehabilitation Intelligent power wheelchairs may enhance participation and improve safety and feelings of well-being for long-term care residents with cognitive impairments. Intelligent power wheelchairs could potentially have an equally positive impact on facility staff, other residents, and family and friends by decreasing workload and increasing safety.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".