Models and frameworks for guiding assessment for aided Augmentative and Alternative communication (AAC): a scoping review
Bibliographic record
Abstract
BACKGROUND: Augmentative and Alternative Communication (AAC) supports individuals with complex communication needs. Conceptual models and frameworks exist to evaluate, implement, and assess the needs of persons with communication disabilities, however, it is unknown which models were grounded in previous evidence-based research. OBJECTIVE: What are the models and frameworks grounded in empirical or conceptual research that enable communication outcomes for persons who require aided AAC systems? ELIGIBILITY CRITERIA: The study had to be the original publication of a defined model or framework that included aided AAC and the model had to be developed through research, either conceptual or empirical. SOURCES OF EVIDENCE: Eleven databases were searched using terms associated with AAC devices, conceptual models, and assessment processes. Fifteen articles presenting 14 independent assessment models were included. CHARTING METHODS: A custom data extraction form included model development using existing models and research evidence, the model's input parameters, and explicit outcome measures. RESULTS: Four models were specific to AAC while ten models were general evaluations for assistive technology systems. Models used a variety of descriptive traits during assessment including: person, technology, environment and context, and the activity or task. Only nine models sought to iteratively assess the client. Eleven of the models identified the inclusion of members from different disciplines in the assessment process. CONCLUSIONS: There is a need to standardize descriptive traits: personal abilities, environmental characteristics, potential assistive technology, and contextual factors. Models should include teams of different disciplines to provide holistic assessments. Models should include outcomes and include iterative solutions.
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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 | 0.054 | 0.128 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.047 | 0.034 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".