Transition from manual to power wheelchair - experiences of persons with Multiple Sclerosis.
Bibliographic record
Abstract
Introduction:During the course of their disease, persons with multiple sclerosis experience restrictions to their mobility that impact on their occupational engagement. Little is known about how persons with multiple sclerosis experience the transition from manual to power wheelchair. Aim:To describe how persons with multiple sclerosis experience the transition from a manual to a power wheelchair, and how this transition affects their occupational engagement. Method: Five persons with multiple sclerosis who had transitioned to a power wheelchair within the previous 36 months participated in semi-structured interviews. The interviews were analysed through qualitative content analysis supported by the framework of the Canadian Model of Occupational Performance and Engagement. Findings:Transition to a power wheelchair improved the participants’ ability to get around, their comfort sitting, and fatigue level. These improvements led to increased occupational engagement, primarily related to leisure activities. The timing of the transition was perceived as an important aspect. Conclusions: A gradual introduction to a power wheelchair earlier in the disease course could facilitate acceptance and help maintain occupational engagement. Significance: The occupational therapist has an important role in raising an early dialogue to find the right timing and processes for transition to power wheelchair to support occupational engagement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".