Spoken Discourse Assessment and Analysis in Aphasia: An International Survey of Current Practices
Bibliographic record
Abstract
Purpose Spoken discourse analysis is commonly employed in the assessment and treatment of people living with aphasia, yet there is no standardization in assessment, analysis, or reporting procedures, thereby precluding comparison/meta-analyses of data and hindering replication of findings. An important first step is to identify current practices in collecting and analyzing spoken discourse in aphasia. Thus, this study surveyed current practices, with the goal of working toward standardizing spoken discourse assessment first in research settings with subsequent implementation into clinical settings. Method A mixed-methods (quantitative and qualitative) survey was publicized to researchers and clinicians around the globe who have collected and/or analyzed spoken discourse data in aphasia. The survey data were collected between September and November 2019. Results Of the 201 individuals who consented to participate, 189 completed all mandatory questions in the survey (with fewer completing nonmandatory response questions). The majority of respondents reported barriers to utilizing discourse including transcription, coding, and analysis. The most common barrier was time (e.g., lack of time). Respondents also indicated that there was a lack of, and a need for, psychometric properties and normative data for spoken discourse use in the assessment and treatment of persons with aphasia. Quantitative and qualitative results are described in detail. Conclusions The current survey study evaluated spoken discourse methods in aphasia across research and clinical settings. Findings from this study will be used to guide development of process standardization in spoken discourse and for the creation of a psychometric and normative property database. Supplemental Material https://doi.org/10.23641/asha.166395100.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".