The effects of adding attention training to naming treatment for individuals with aphasia
Bibliographic record
Abstract
AIM: Given the ever-increasing evidence for the co-occurrence of attention impairments and language disorders in chronic aphasia, this study aimed to compare the effects of two naming treatment programs, one with and one without attention training components, on the naming performance of participants with aphasia. MATERIALS AND METHODS: This was a single-subject crossover study in which six people with chronic aphasia and different degrees of naming and attention impairments participated. Two treatment programs were implemented for each participant, with the sequence of the treatments with crossover design. Each program consisted of 12 treatment sessions plus pre- and post-treatment assessment sessions (15 weeks for each participant). The visual analysis and WEighted STatistics methods were employed for data analysis. RESULTS: Based on visual analysis, both treatments improved in comparison to the baseline phase. Statistical analysis revealed that the number of participants with significant naming improvement following combined program (5 participants) was larger than the number of participants showing improvement following completion of the single, program. CONCLUSION: Although integrating attention training into a conventional treatment for anomia can increase the effect of treatment on naming ability, more studies are required to clarify the role of attention in remediating naming impairments in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".