Let's Chat About Spoken Discourse: A Tutorial to Support Use of Spoken Discourse Analysis When Providing Aphasia Clinical Services
Bibliographic record
Abstract
PURPOSE: Spoken discourse is integral to everyday communication; improving discourse outcomes is a primary goal for individuals with aphasia and their families. Consequently, the application of discourse analysis in aphasia assessment and treatment has gained increasing attention in both research and clinical settings. Despite its recognized value among researchers and clinicians, several barriers-such as limited time, inadequate training, and lack of resources-continue to impede the widespread use of discourse analysis into clinical practice. To facilitate its broader adoption, speech-language pathologists require access to comprehensive resources that include information on discourse tasks, outcome measures, psychometric properties, and practical examples of how to implement spoken discourse assessments effectively. The purpose of this tutorial is to equip clinicians with this knowledge, promoting the consistent and effective application of discourse analysis in clinical settings. METHOD: This tutorial, developed by members of the FOQUSAphasia Writing Group-comprising both researchers and clinical practitioners-offers an overview of recommended spoken discourse collection and analysis procedures, outcome measures, and their psychometric properties, as well as factors to consider when planning to conduct discourse assessments. It includes a series of case studies (severe aphasia, latent or very mild aphasia, bilingual aphasia, and primary progressive aphasia) that illustrate the utility of discourse analysis for varied clinical contexts and shows how the choice of tasks and measures can reveal meaningful insights tailored to the individual being assessed. In addition, the tutorial provides practical recommendations and considerations for incorporating discourse analysis into clinical aphasia services, along with suggestions for future research. CONCLUSIONS: Spoken discourse production can be an important indicator of communication ability in individuals with aphasia. This tutorial is intended to support clinicians by providing evidence-based, practical ways for integrating discourse analysis into aphasia assessment and treatment. Our collation of information and case studies should encourage clinicians to apply spoken discourse-based approaches, ultimately improving outcomes for individuals with aphasia. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29287505.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".