A scoping review of powered wheelchair driving tasks and performance-based outcomes
Bibliographic record
Abstract
Introduction: Wheelchairs and scooters promote participation and have positive effects on quality of life. However, powered wheelchairs (PW) use can be challenging and can pose safety concerns. Adequate PW assessment and training is important. There is a wide variety of tasks and PW driving assessment measures used for training and assessing PW driving ability in the literature and little consensus as to which tasks and outcomes are the most relevant. A scoping review of the literature was performed in order to characterize this extensive variety of tasks and performance-based outcomes used for training and assessing PW skills.Methods: A search of the literature was conducted in January 2017. Four databases were searched: CINAHL, Embase, PsycInfo and Medline. Articles were included if they contained at least one PW driving task.Results: 827 articles were screened and 48 articles were retained. PW driving tasks from each article were identified and categorized in terms of the environment in which they were performed: Driving in a controlled environment, ecological driving tasks, 2D virtual environment (VE) tasks, 3D VE tasks. The assessment measures formed a separate category. Subjective and objective performance-based outcomes related to PW driving were also identified and grouped into outcomes assessing speed and outcomes assessing accuracy.Conclusion: This scoping review provides an overview of tasks and performance outcomes used in the literature when training and assessing PW skills. The results of this review could guide future research when choosing appropriate tasks and performance outcomes for PW driving ability.Implications for rehabilitationThere is wide variety of tasks and performance-based outcomes for PW driving.Results showed that available assessment measures are not commonly used in research and that tasks used often lacked consistency across studies.New methods to measure the interaction of speed and accuracy are needed.The contents of this review could be used by researchers as a starting point when designing a PW task and selecting appropriate performance-based outcomes.
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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 | 0.021 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".