Impact of the TEAM Wheels eHealth manual wheelchair training program: Study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Variable, and typically inadequate, delivery of skills training following manual wheelchair (MWC) provision has a detrimental impact on user mobility and participation. Traditional in-person delivery of training by rehabilitation therapists has diminished due to cost, travel time, and most recently social distancing restrictions due to COVID-19. Effective alternative training approaches include eHealth home training applications and interactive peer-led training using experienced and proficient MWC users. An innovative TEAM Wheels program integrates app-based self-training and teleconference peer-led training using a computer tablet platform. OBJECTIVE: This protocol outlines implementation and evaluation of the TEAM Wheels training program in a randomized control trial using a wait-list control group. SETTING: The study will be implemented in a community setting in three Canadian cities. PARTICIPANTS: Individuals ≥ 18 years of age within one year of transitioning to use of a MWC. INTERVENTION: Using a computer tablet, participants engage in three peer-led teleconference training sessions and 75-150 minutes of weekly practice using a video-based training application over 4 weeks. Peer trainers individualize the participants' training plans and monitor their tablet-based training activity online. Control group participants also receive the intervention following a 1-month wait-list period and data collection. MEASUREMENTS: Outcomes assessing participation; skill capacity and performance; self-efficacy; mobility; and quality of life will be measured at baseline and post-treatment, and at 6-month follow-up for the treatment group. IMPACT STATEMENT: We anticipate that TEAM Wheels will be successfully carried out at all sites and participants will demonstrate statistically significant improvement in the outcome measures compared with the control group.
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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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.082 | 0.014 |
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".