Team Consensus Concerning Important Outcomes for Augmentative and Alternative Communication Assistive Technologies: A Pilot Study
Bibliographic record
Abstract
Obstacles to assistive device outcome measurement include a lack of consensus about which outcomes should be evaluated. This article reports a case study of the use of a structured consensus-building approach called Technique for Research of Information by Animation of a Group of Experts (TRIAGE) to develop agreement among key professional team members with regard to outcome measurement. We also describe the changes in key professional team members' perspectives on outcome measurement over time. Initially, participants expressed preferences for the measurement of about 33 different outcomes. Subsequent discussions and the TRIAGE process led to the choice of the five most important outcomes. Our case study provides evidence that professional team consensus could successfully be reached through the individual reflections and group sharing proposed by the TRIAGE technique. Future research directions include the development of strategies to give prominence to the opinions of individuals who use augmentative and alternative communication (AAC) in the identification of important outcomes, and for aggregating and interpreting data gathered at local, regional, or national levels.
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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 | 0.069 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".