An International Survey of Assessment Practices for Short-Term and Working Memory Deficits in Aphasia
Bibliographic record
Abstract
Purpose: Recent research has highlighted the clinical relevance of understanding the nature of short-term memory (STM) and working memory (WM) deficits in persons with aphasia and the way these deficits affect linguistic processing and functional communication in activities of daily living. The psychometric properties of tests commonly used to identify STM/WM problems in individuals with aphasia, however, have been questioned. No previous study has sought to investigate assessment practices and attitudes by speech-language pathologists involved in aphasia management. Accordingly, the aims of this study were (a) to investigate both attitudes toward STM/WM assessment in individuals with aphasia, as well as the types and frequency of STM/WM tests used with individuals with aphasia, and (b) to explore factors (e.g., educational background) that may influence STM/WM assessment practices. Method: Respondents recruited via professional and aphasia support organizations completed an online survey. The survey elicited information about the respondents' demographic and clinical backgrounds and STM/WM assessment clinical practices and views, including frequency and preferred use of specific STM/WM tests. Results: The majority of respondents reported regular use of STM/WM tests as part of aphasia management. Positive attitudes toward STM/WM assessments were also reported. The most popular rankings of tests were the Cognitive Linguistic Quick Test (Helm-Estabrooks, 2001), the Comprehensive Aphasia Test (Swinburn, Porter, & Howard, 2005), and the Token Test (McNeil & Prescott, 1978). Results suggested limited knowledge about measures that assess self-perceptions of functional memory abilities. Regression analyses showed that the frequency of reported STM/WM test use was similar between clinicians and dual-role researchers/clinicians, but their attitudes toward the value of STM/WM differed. U.S. and UK respondents reported similar assessment practices. Conclusions: It is reassuring that STM/WM is taken into consideration by clinicians when providing aphasia management. Two of the most popular tests, however, have poor psychometric properties, and caution should be exercised in clinical decision making. The different value placed on STM/WM testing by clinicians and researchers/clinicians has implications for continuing professional development.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".