Case Studies in Pre-Service AAC Instruction: Comforting the Client While Stressing the Student
Bibliographic record
Abstract
Abstract Teaching speech-language pathology (SLP) students about alternative and augmentative communication (AAC) through case studies can provide a more meaningful experience than found in a more traditional didactic approach. Case studies give students a clinical context for the material presented in class. They also enable consideration of a wide range of factors, including family dynamics, school or work contexts, and the participation of other team members (e.g., POT, PT, and teachers). In this course, case studies are the focus, but material is also presented through lecture/discussion, labs (where various AAC devices are used and evaluated by the students), and readings. The focus on case studies presents a number of challenges. For the students, this is one of the first times they are forced to deal with complex clinical problems for which the answers are not readily available in a textbook. They complain that the assignments are vague and that the cases require too much time to complete. For the instructors, the course requires much more time in providing information to the students, answering questions about the cases, and generally supporting the students. In the end, the students manage to “pull it all together” and present thoughtful and thorough implementation plans for their cases. After entering into practice or graduating, students report that the course prepared them for working with a client with AAC needs.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".