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Record W2017248361 · doi:10.1310/cl3v-a94a-de5c-cvbe

A Set of Observational Measures for Rating Support and Participation in Conversation Between Adults with Aphasia and Their Conversation Partners

2004· article· en· W2017248361 on OpenAlexaff
Aura Kagan, Joanne Winckel, Sandra E. Black, Judith Felson Duchan, Nina Simmons‐Mackie, Paula A. Square

Bibliographic record

VenueTopics in Stroke Rehabilitation · 2004
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsConversationAphasiaPsychologySet (abstract data type)Observational studyConversation analysisLinguisticsCognitive psychologyMedicineCommunicationComputer science

Abstract

fetched live from OpenAlex

Conversation partners of individuals with aphasia, including health care professionals, families, and others, play a role that is as important for communication as the language disorder suffered by individuals with aphasia. Two complementary measures designed to capture elements of conversation between adults with aphasia and their speaking conversation partners have been developed. The first measure provides an index of the conversation partner's skill in providing conversational support. The second provides an index of the level of participation in conversation by the person with aphasia. This article describes the development of the measures, including preliminary psychometric data, and discusses applications.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.067
GPT teacher head0.347
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations142
Published2004
Admission routes1
Has abstractyes

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Same venueTopics in Stroke RehabilitationSame topicNeurobiology of Language and BilingualismFrench-language works237,207