The role of language proficiency and linguistic distance in cross-linguistic treatment effects in aphasia
Bibliographic record
Abstract
Current findings from intervention in bilingual aphasia are inconclusive regarding the extent to which levels of language proficiency and degree of linguistic distance between treated and non-treated languages influence cross-language generalisation and changes in levels of language activation and inhibition following treatment. In this study, we enrolled a 65-year-old multilingual speaker with aphasia and administered treatment in his L1, Dutch. We assessed pre- and post-treatment performance for seven of his languages, five of high proficiency and two of lower proficiency. We asked whether treatment in L1 would generalise to his other languages or increase interference among them. Forty hours of treatment were completed over the course of five weeks. Each language was tested three times at pretreatment and at post-treatment. Testing included measures of narrative production, answering questions, picture description and question generation. Dependent measures examined language efficiency, defined as Correct Information Units (CIUs)/min, as well as language mixing, defined as proportion of code-mixed whole words. We found that our participant's improved efficiency in Dutch was mirrored by parallel improvement in the four languages of high proficiency: English, German, Italian and French. In contrast, in his languages of lower proficiency, Norwegian and Spanish, improved efficiency was limited. An increase in code-mixing was noted in Spanish, but not in Norwegian. We interpret the increased code-mixing in Spanish as indication of heightened inhibition following improvement in a language of close linguistic proximity, Italian. We conclude that an interaction of language proficiency and linguistic similarity affects cross-language generalisation following intervention in multilingual aphasia.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".