The effects of symbolic gestural training on enhancing recovery of spoken naming in people with aphasia: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate the effects of symbolic gestural training on enhancing recovery of spoken naming in people with aphasia (PWA) using a systematic review and meta-analysis. METHOD: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, article search was conducted from four databases: Web of Science Core Collection, Medline, PsycINFO, and EBSCO. A total of 45 participants from four studies investigating the symbolic gestural training effects on PWA and outcome measures of spoken naming were included. RESULT: The meta-analysis showed a medium overall effect of symbolic gestural training on enhancing recovery of spoken naming in PWA. Subgroup analysis also revealed that the training effect was more remarkable in the gesture + verbal training paradigm than in the gesture-only training paradigm. However, the differences in the training effects between short and long duration, and training supplied with and without feedback, were nonsignificant. CONCLUSION: This study illustrates the current state of the literature on symbolic gestural training in PWA, and serves as a reference for clinicians, patients, and health policy-makers regarding the application of symbolic gestural training in clinical or rehabilitation programs.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".