‘Power in Mobility’: parent and therapist perspectives of the experiences of children learning to use powered mobility
Bibliographic record
Abstract
AIM: The aim of this study was to gain insights, from the perspectives of both parents and pediatric therapists, into the experiences of children learning to use a power mobility device. METHOD: The purposive sample included 33 participants: 14 parents of children who were learning, or had learned, to use a power mobility device and 19 pediatric occupational therapists or physical therapists. Data were gathered face-to-face via seven focus groups consisting of either parents or therapists, and eight one-on-one interviews. Data were analyzed using the constant comparative method. RESULTS: Three main themes were identified: (1) 'Power in mobility' described how learning to use powered mobility changed more than just a child's locomotor abilities; (2) 'There is no recipe' revealed how learning to use powered mobility occurred along an individualized continuum of skills that often unfolded over time in a cyclical process; (3) 'Emotional journey' explored how learning to use powered mobility was an emotionally charged undertaking for all those involved. INTERPRETATION: Learning to use a power mobility device is a complex process that often requires perseverance and determination on the part of the child, family, and therapist. WHAT THIS PAPER ADDS: Powered mobility use impacts more than just a child's locomotor abilities. Learning to use a power mobility device is a highly individualized process. Learning to use powered mobility may be an emotionally charged process.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".