Research priorities in augmentative and alternative communication as identified by people who use AAC and their facilitators
Bibliographic record
Abstract
Two focus groups comprised of adults who used AAC and two focus groups comprised of adult AAC facilitators in Ontario, Canada were asked to identify their own AAC research priorities and to state their levels of agreement with previously identified research priorities in AAC. Members of the focus group who used AAC had physical disabilities since birth except one participant who became disabled at age 2 years. Using focus group methodology and analysis, the participants were asked to generate their own AAC research priorities. A questionnaire and Likert-type scale was used to determine their levels of agreement with six research priorities set a decade earlier by a group of AAC researchers sponsored by the United States-based National Institute of Deafness and other Communication Disorders (NIDCD). Focus group members stressed the importance of (a) preparing people who use AAC to succeed in situations such as maintaining friendships, dating, and finding jobs; (b) improving service delivery of their AAC devices; (c) improving technology in high tech and low tech devices; (d) increasing public awareness of people who use AAC; (e) improving methods of teaching reading skills to people who use AAC; and (f) improving AAC communications training for all healthcare professionals.
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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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".