Telehealth Assessments and Interventions for Individuals With Poststroke Aphasia: A Scoping Review
Bibliographic record
Abstract
PURPOSE: There are increasing demands for aphasia assessment and intervention services to be delivered remotely. The purpose of this scoping review was to address what is known about the delivery of assessments and interventions using telehealth for people with poststroke aphasia. Specifically, the review sought to (a) identify which telehealth assessment protocols have been used, (b) identify which telehealth intervention protocols have been used, and (c) describe evidence on the effectiveness and feasibility of telehealth for people with poststroke aphasia. METHOD: A scoping review of the literature published in English since 2013 was conducted by searching MEDLINE, Embase, PsycINFO, CINAHL, and Scopus databases to identify relevant studies. A total of 869 articles were identified. Two reviewers screened records independently, finding 25 articles eligible for inclusion. Data extraction was conducted once and validated by the second reviewer. RESULTS: Two of the included studies examined telehealth assessment protocols, whereas the remaining studies focused on the delivery of telehealth interventions. The results of the included studies illustrated both effectiveness and feasibility regarding telehealth for people with poststroke aphasia. However, a lack of procedural variation among the studies was found. CONCLUSIONS: Overall, this scoping review yielded continued support for the use of telehealth practices as an alternate mode of delivering both assessment and intervention services to people with poststroke aphasia. However, further research is needed to investigate the range of aphasia assessment and intervention protocols that can be offered via telehealth, such as assessments or interventions that use patient-reported measures or address extralinguistic cognitive abilities.
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".