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Record W2054967778 · doi:10.1155/2011/131820

Clinical Focus on Prosodic, Discursive and Pragmatic Treatment for Right Hemisphere Damaged Adults: What's Right?

2011· article· en· W2054967778 on OpenAlexafffund
Perrine Ferré, Bernadette Ska, Camille Lajoie, Amélie Bleau, Yves Joanette

Bibliographic record

VenueRehabilitation Research and Practice · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsIntervention (counseling)PragmaticsRight hemisphereFocus (optics)MedicineSemantics (computer science)RehabilitationLinguisticsLateralization of brain functionPsychologyCognitive psychologyComputer scienceNursingPhysical therapy

Abstract

fetched live from OpenAlex

Researchers and clinicians acknowledge today that the contribution of both cerebral hemispheres is necessary to a full and adequate verbal communication. Indeed, it is estimated that at least 50% of right brain damaged individuals display impairments of prosodic, discourse, pragmatics and/or lexical semantics dimensions of communication. Since the 1990's, researchers have focused on the description and the assessment of these impairments and it is only recently that authors have shown interest in planning specific intervention approaches. However, therapists in rehabilitation settings still have very few available tools. This review of recent literature demonstrates that, even though theoretical knowledge needs further methodological investigation, intervention guidelines can be identified to target right hemisphere damage communication impairments in clinical practice. These principles can be incorporated by speech and language pathologists, in a structured intervention framework, aiming at fully addressing prosodic, discursive and pragmatic components of communication.

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)0.003
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.121
GPT teacher head0.451
Teacher spread0.330 · 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.

Study designOther design
Domainnot available
GenreEmpirical

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

Citations42
Published2011
Admission routes2
Has abstractyes

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