Implementation of the International Classification of Functioning, Disability, and Health (ICF) Core Sets for Children and Youth with Cerebral Palsy: Global Initiatives Promoting Optimal Functioning
Bibliographic record
Abstract
Background: The International Classification of Functioning, Disability, and Health (ICF) Core Sets for children and youth with cerebral palsy (CP) offer service providers and stakeholders a specific framework to explore functioning and disability for assessment, treatment, evaluation, and policy purposes in a global context. Objective: Describe global initiatives applying the ICF Core Sets for children and youth with CP, with a focus on contributions to clinical practice and challenges in their implementation. Methods: This is a descriptive cross-sectional study. Ongoing initiatives applying the ICF Core Sets for CP in Russia, Poland, Malawi, and Brazil are included. Results: The main contributions of applying the ICF Core Sets for children and youth with CP include: (1) an objective description of abilities and limitations in everyday activities; (2) a consistent identification of facilitators and barriers influencing functioning; (3) a practical communication tool promoting client-centered care and multidisciplinary teamwork; and, (4) a useful guideline for measurement selection. The main challenges of adopting the ICF Core Sets are related to lack of ICF knowledge requiring intense training and translating results from standardized measures into the ICF qualifiers in a consistent way. Conclusions: Global initiatives include research and clinical applications at the program, service and system levels. The ICF Core Sets for CP are useful tools to guide service provision and build profiles of functioning and disability. Global interprofessional collaboration, capacity training, and informatics (e-records) will maximize their applications and accelerate adoption.
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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 | 0.041 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".