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Record W4393410107 · doi:10.1080/17549507.2024.2321939

The effects of symbolic gestural training on enhancing recovery of spoken naming in people with aphasia: A systematic review and meta-analysis

2024· review· en· W4393410107 on OpenAlexaff
Wei Zhang, Liao Yi

Bibliographic record

VenueInternational Journal of Speech-Language Pathology · 2024
Typereview
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsMcMaster UniversityCarleton University
Fundersnot available
KeywordsGestureAphasiaPsycINFOMeta-analysisPsychologyTraining (meteorology)MEDLINERehabilitationSystematic reviewComputer scienceCognitive psychologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.496
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.388
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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