Aphasia Therapy in the Age of Globalization: Cross-Linguistic Therapy Effects in Bilingual Aphasia
Bibliographic record
Abstract
INTRODUCTION: Globalization imposes challenges to the field of behavioural neurology, among which is an increase in the prevalence of bilingual aphasia. Thus, aphasiologists have increasingly focused on bilingual aphasia therapy and, more recently, on the identification of the most efficient procedures for triggering language recovery in bilinguals with aphasia. Therapy in both languages is often not available, and, thus, researchers have focused on the transfer of therapy effects from the treated language to the untreated one. AIM: This paper discusses the literature on bilingual aphasia therapy, with a focus on cross-linguistic therapy effects from the language in which therapy is provided to the untreated language. METHODS: Fifteen articles including two systematic reviews, providing details on pre- and posttherapy in the adult bilingual population with poststroke aphasia and anomia are discussed with regard to variables that can influence the presence or absence of cross-linguistic transfer of therapy effects. RESULTS AND DISCUSSION: . The potential for CLT of therapy effects from the treated to the untreated language depends on the word type, the degree of structural overlap between languages, the type of therapy approach, the pre- and postmorbid language proficiency profiles, and the status of the cognitive control circuit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".