GLOBAL EXPERIENCE IN CONTINUOUS ASSESSMENT OF THE EFFECTIVENESS OF REHABILITATION OF CHILDREN WITH DISABILITIES
Bibliographic record
Abstract
Continuous assessment of rehabilitation effectiveness in children with disabilities has become a cornerstone of modern pediatric rehabilitation systems worldwide. This literature review synthesizes findings from ten peer-reviewed studies published between 2015 and 2025 in Scopus and Web of Science databases, exploring global experiences, methodologies, and frameworks for evaluating rehabilitation outcomes. The studies emphasize the use of standardized assessment instruments such as the Pediatric Evaluation of Disability Inventory (PEDI), WeeFIM, ICF-CY-based models, and Goal Attainment Scaling (GAS), which enable objective and dynamic measurement of progress in motor, cognitive, and social domains. Developed countries, including the United States, Canada, and Japan, have integrated digital and tele-rehabilitation platforms that allow real-time monitoring and adaptive goal-setting for each child. Meanwhile, low- and middle-income countries have increasingly adopted WHO’s International Classification of Functioning, Disability and Health (ICF) framework to establish baseline data and monitor long-term outcomes within resource-limited settings. Evidence across studies indicates that continuous, multi-dimensional assessment leads to improved care coordination, more individualized rehabilitation plans, and better long-term functional outcomes. Furthermore, integrating caregivers’ feedback and interdisciplinary collaboration enhances the sustainability of interventions. Despite advances, disparities in access to standardized assessment tools and digital monitoring technologies persist, especially in developing regions. The global experience highlights that systematic, data-driven, and family-centered assessment approaches are essential to optimizing rehabilitation quality and equity for children with disabilities.
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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 | 0.047 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".