A Proposed Functional Abilities Classification Tool for Developmental Disorders Affecting Learning and Behaviour
Bibliographic record
Abstract
Children with developmental disorders affecting learning and behaviour (e.g. attention, social communication, language and learning disabilities etc.) require individualized support across multiple environments to promote participation, quality of life and developmental outcomes. Support to enhance participation is based largely on individual profiles of functioning (e.g. communication, cognitive, social skills, executive functioning etc.), which are highly heterogeneous within medical diagnoses. Currently educators, clinicians and parents encounter widespread difficulties in meeting children’s needs as there is lack of universal classification of functioning and disability for use in school environments. Objective: A practical tool for functional classification broadly applicable for children with developmental disorders affecting learning and behaviour could facilitate the collaboration, identification of points of entry of support, individual program planning and reassessment in a transparent, equitable process based on functional need and context. We propose such a tool, the Functional Abilities Classification Tool (FACT) based on the concepts of the ICF (International Classification of Functioning, Disability and Health). FACT is intended to provide ability and participation classification that is complementary to medical diagnosis. For children presenting with difficulties, the proposed tool initially classifies participation over several environments. Then, functional abilities are classified and personal factors and environment are described. Points of entry for support are identified given an analysis of functional ability profile, personal factors, environmental features and pattern of participation. Conclusion: Case examples, use of the tool and implications for children, agencies and the system are described.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".