Developing a Culturally Sensitive ICF-Based Tool to Describe Functioning of Children with Autism Spectrum Disorder: TEA-CIFunciona Version 1.0 Pilot Study
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorder (ASD) affects the daily functioning of children and their families; however, in Argentina, there are no standardized tools to guide the description, evaluation, and follow-up of functioning and disability of children with ASD. To fill this gap, the overarching purpose of this study was to create a novel tool guided by the International Classification of Functioning, Disability, and Health (ICF) Core Sets for ASD for clinical practice. METHODS: A multistep methodology was used to identify the most relevant ICF categories for an Argentinian clinical setting. The content of this ICF-based shortlist was piloted and revised according to the results. Subsequently, a toolbox of measures was proposed to operationalize each ICF category. Finally, profiles of the functioning of 100 children with ASD were created. RESULTS: An ICF-based tool called TEA-CIFunciona was created, consisting of 32 ICF categories (10 body functions, 15 activities and participation, 7 environmental factors categories). The application of TEA-CIFunciona incorporated a family-centered approach in ASD evaluations and helped identify functional needs. CONCLUSIONS: TEA-CIFunciona is the first ICF-based instrument that guides the description of functioning of children with ASD in Argentina. TEA-CIFunciona standardizes collaborative assessments in pediatric ASD populations in Latin American contexts.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".