A Novel Morphology-Based Naming Therapy for People with Aphasia
Bibliographic record
Abstract
Background Previous studies have demonstrated that naming treatments can improve language abilities in people with aphasia (PWA). However, there is currently a lack of protocols for evidence-based naming treatment in Hebrew.Aims This study aims to evaluate the efficacy of a novel morphology-based naming treatment for Hebrew-speaking PWA and to investigate subject-related factors influence responsiveness to the treatment.Method & Procedures Twelve chronic stroke PWA and moderate to severe anomia participated in 20 treatment sessions focused on the root-structure morphology of Hebrew. Treatment stimuli incorporated morphologically complex words comprising root and template. Treatment effects were assessed at both subject level and group level.Outcomes & Results The treatment showed promising results, with a significant increase in correct naming for both treated and untreated complex words. These gains were maintained for at least 10 weeks post-treatment. Most of the benefit was achieved during the first 10 treatment sessions. Additionally, the group demonstrated generalization effects to naming simple words. Pre-treatment performance in naming morphologically complex words predicted higher treatment gains during the follow-up session, irrespective of word type.Conclusions These findings provide preliminary evidence supporting the efficacy of root-based naming treatment for Hebrew-speaking PWA. Future research should compare this treatment to an untreated control group and to other treatment methods in Hebrew speakers to further validate its benefits.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".