Telerehabilitation for Children and Youth with Developmental Disabilities and Their Families: A Systematic Review
Bibliographic record
Abstract
AIM: To determine the level of evidence for the effectiveness of telerehabilitation against comparison interventions in improving child- and parent-related outcomes in children and youth with developmental disabilities. METHOD: A systematic approach, comprised of a comprehensive search; transparent study selection, data extraction, quality assessment by independent reviewers; and synthesis of sufficiently similar data (per diagnostic group, health profession, and overall level of evidence for each outcome) was undertaken. RESULTS: Fifty-five studies (29 randomized trials) were included across six diagnostic groups and ten health professions. Common telerehabilitation targets varied across diagnostic groups and included motor function, behavior, language, and parental self-efficacy. Telerehabilitation was found to be either more effective or as effective versus comparison intervention in improving 46.9% or 53.1% of outcomes, respectively. It was never found to be detrimental or less effective. Strong to moderate, limited, and insufficient levels of evidence were found for 36.5%, 24.5%, and 38.6% of the outcomes, respectively. CONCLUSION: There is sufficient evidence suggesting that telerehabilitation is a promising alternative when face-to-face care is limited. It is comparable to usual care and is more effective than no treatment. Blending in-person and telerehabilitation approaches could be beneficial for the post-pandemic future of rehabilitation in pediatric care.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".