Power Mobility Driving Training for Seniors: A Pilot Study
Bibliographic record
Abstract
This article describes two power mobility training protocols used with seniors and compares posttraining driving performance. Twelve users of power mobility were consecutively recruited from two residential facilities in Toronto, Canada. The aim of training at both sites was to make clients comfortable with and safe at driving power mobility devices. The content of training was similar, but training protocols differed significantly in terms of the number of sessions (means of 3.43 vs. 9.80; p < or = .05) and the time frame over which the sessions were offered (means of 1.57 vs. 5.10 weeks; p < or = .01). Participants at the two sites differed significantly in terms of overall driving performance (p < or = .05), gender (p < or = .01), and type of device used (p < or = .05). Overall, driving performance was significantly associated with facility, gender, type of device used, and training duration (p < or = .05). When these variables were entered into an exploratory hierarchical regression, facility accounted for 64% of the variance in driving performance. When facility was controlled for, the correlations between device and duration of training with driving performance were no longer significant. The determinants of driving performance are difficult to clearly specify as the variable facility encompasses gender as well as all other differences between the two training protocols. Nevertheless, these data provide direction for future research in this area.
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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 | 0.004 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".