Mobility Intensive Training (Mob-IT) Protocol for Children with Cerebral Palsy: Feasibility and Fidelity Results
Bibliographic record
Abstract
The Mobility Intensive Training (Mob-IT) protocol is an innovative intervention focused on motor learning to improve the mobility of children with cerebral palsy (CP). The objective was to describe the feasibility and intervention fidelity of Mob-IT. A single-subject experimental study was conducted with four children with CP, a median age of 11 (7–13) years, and a Gross Motor Function Classification System I–III. The Mob-IT included 24 h of practice of mobility goals, delivered three times a week in 2 h sessions over four weeks. Feasibility was assessed using the Qualitative Feedback Questionnaire (QFQ), evaluating adherence, acceptability, adverse effects, the clarity of procedures, and intervention time. The Canadian Occupational Performance Measure (COPM) was used to assess participant and caregiver satisfaction. Fidelity was measured by the type of feedback provided (intrinsic vs. extrinsic), task challenge level, and intervention volume. Participants reported good acceptance, few adverse effects, and satisfaction with the outcomes. The intervention adhered to the proposed principles, with a focus on extrinsic feedback and tasks showing progression over time. Time was well spent, being 78% focused on activities and using mostly extrinsic-focused feedback. The Mob-IT protocol was considered feasible and faithful to its principles. As this is a feasibility study, the results indicate the need to expand the intervention to a larger, randomized study.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.009 | 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".