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Record W2029193922 · doi:10.1310/tsr1405-28

Motor Learning and the Use of Videotape Feedback After Stroke

2007· article· en· W2029193922 on OpenAlexaffabout
Paula E. Gilmore, Sandi J. Spaulding

Bibliographic record

VenueTopics in Stroke Rehabilitation · 2007
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsWestern University
Fundersnot available
KeywordsSOCKSPsychologyMotor learningOccupational therapyPhysical medicine and rehabilitationPhysical therapyRehabilitationSignificant differenceStroke (engine)Motor skillMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Efforts have been made to apply motor learning theories to the rehabilitation of individuals following stroke. Motor learning poststroke has not been well investigated in the literature. This research attempted to fill the gap regarding motor learning applied to practice. PURPOSE: This two-group research study attempted to determine the effectiveness of an experimental therapy combining videotape feedback with occupational therapy compared to only occupational therapy in learning the motor skill of donning socks and shoes after stroke. METHOD: Ten participants were randomly assigned to one of the two groups and all participants were videotaped during pretest and up to 10 treatment sessions aimed at donning socks and shoes. Only one group viewed their videotape replay. The acquisition of donning socks and shoes was measured using the socks and shoes subtests of the Klein-Bell Activities of Daily Living Scale and their scores on the Canadian Occupational Performance Measure. RESULTS: There was no significant difference between the two groups and both groups improved. However, the group that received videotape feedback thought they performed better and were more satisfied with their ability to don shoes, lending support for the use of videotape feedback poststroke to improve satisfaction with performance.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.659
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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 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

Citations25
Published2007
Admission routes2
Has abstractyes

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