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Record W1588595169 · doi:10.5195/ijt.2014.6157

Combining Teletherapy and On-line Language Exercises in the Treatment of Chronic Aphasia: An Outcome Study

2015· article· en· W1588595169 on OpenAlexaff
Richard Steele, Allison Baird, Denise McCall, Lisa Haynes

Bibliographic record

VenueInternational Journal of Telerehabilitation · 2015
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsManitoba Beekeepers' Association
Fundersnot available
KeywordsAphasiaAshaConfidence intervalRating scalePhysical therapyPsychologyPollingAudiologyMedicineComputer scienceLinguisticsPsychiatryDevelopmental psychologyInternal medicine

Abstract

fetched live from OpenAlex

We report a 12-week outcome study in which nine persons with long-term chronic aphasia received individual and group speech-language teletherapy services, and also used on-line language exercises to practice from home between therapy sessions. Participants were assessed at study initiation and completion using the Western Aphasia Battery, a portion of the Communicative Effectiveness Index, ASHA National Outcome Measurement System, and RIC Communication Confidence Rating Scale for Aphasia; additionally participants were polled regarding satisfaction at discharge. Pretreatment and post-treatment means were calculated and compared, and matched t-tests were used to determine significance of improvements following treatment, with patterns of independent on-line activity analyzed. Analysis of scores shows that means improved on most measures following treatment, generally significantly: the WAB AQ improved +3.5 (p = .057); the CETI Overall (of items administered) - +17.8 (p = .01), and CCRSA Overall - + 10.4 (p = .0004). Independent work increased with time, and user satisfaction following participation was high.

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.414
Teacher spread0.350 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

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