Group interventions for people with primary progressive aphasia and their care partners: Considerations for clinical practice
Bibliographic record
Abstract
Primary progressive aphasia (PPA) describes a group of language-led dementias. Speech and language therapy is the main available intervention for people with PPA. Despite best practice recommendations for speech and language therapy to include access to group therapies (Volkmer et al, 2023a), research evidence to date has predominantly focused on delivery in individual sessions. The aim of this study was to gather the collective intelligence of expert speech and language therapists/pathologists delivering group therapy for people with PPA to synthesize guidance for clinicians. This paper describes a qualitative study using narrative synthesis methods. Data were collected using the Template for Intervention Description and Replication - TIDiER. Eight respondents described a total of 17 different groups. Respondents worked across healthcare, research clinics and third sector organizations in Australia, Canada, Spain, the USA and the UK. For the purposes of analysis, groups were divided into two main types: (1) groups delivering specific therapy interventions; and (2) groups providing broader opportunities for conversational practice and support. This initial synthesis of the current state of the art in PPA therapy groups highlights several important considerations around candidacy, content and ecological validity of delivering group intervention for people with PPA.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".