Global perspectives on the management of primary progressive aphasia
Bibliographic record
Abstract
Speech-language therapists/pathologists (SLT/Ps) are key professionals in the management and treatment of primary progressive aphasia (PPA), however, there are gaps in education and training within the discipline, with implications for skills, confidence, and clinical decision-making. This survey aimed to explore the areas of need amongst SLT/Ps working with people living with PPA (PwPPA) internationally to upskill the current and future workforce working with progressive communication disorders. One hundred eighty-six SLT/Ps from 27 countries who work with PwPPA participated in an anonymous online survey about their educational and clinical experiences, clinical decision-making, and self-reported areas of need when working with this population. Best practice principles for SLT/Ps working with PwPPA were used to frame the latter two sections of this survey. Only 40.7% of respondents indicated that their university education prepared them for their current work with PwPPA. Competency areas of "knowing people deeply," "practical issues," "connectedness," and "preventing disasters" were identified as the basic areas of priority and need. Respondents identified instructional online courses (92.5%), sample tools and activities for interventions (64.8%), and concrete training on providing care for advanced stages and end of life (58.3%) as central areas of need in their current work. This is the first international survey to comprehensively explore the perspectives of SLT/Ps working with PwPPA. Based on survey outcomes, there is a pressing need to enhance current educational and ongoing training opportunities to better promote the well-being of PwPPA and their families, and to ensure appropriate preparation of the current and future SLT/P workforce.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".