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Record W2116444006 · doi:10.1177/030802260406701106

Improvement in Upper Limb Motor Performance following Stroke: The Use of Mental Practice

2004· article· en· W2116444006 on OpenAlexfundno aff
Alison R Bell, Bridget J Murray

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2004
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
FundersMcMaster University
KeywordsRehabilitationStroke (engine)Physical medicine and rehabilitationPsychologyMotor imageryDancePremiseCognitionMotor learningMotor skillClinical PracticePhysical therapyMedicinePsychiatryNeuroscience

Abstract

fetched live from OpenAlex

Mental practice is a technique that involves imagery and rehearsal of movement without movement actually occurring. This study considers the evidence that indicates whether mental practice is successful in improving upper limb motor performance after a stroke. The use of mental practice in the traditional fields of sport, music and dance is identified and a theoretical premise for its application in stroke rehabilitation is presented. Eight studies on the use of mental practice in the rehabilitation of motor performance following a stroke are critiqued. These studies suggest that mental practice improves upper limb motor ability and appears to be applicable to a range of participants, especially those with moderate impairment, although good cognitive and communication skills are required. The article suggests reasons that this relatively new approach should be considered by occupational therapists involved in stroke rehabilitation. The limited number of studies and small sample sizes are highlighted. Further research is recommended in order to identify people who will benefit from mental practice, to investigate the generalisation of results and to establish guidelines for the effective provision of mental practice in terms of length, format and content in stroke rehabilitation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.308
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.077
GPT teacher head0.372
Teacher spread0.295 · 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

Citations8
Published2004
Admission routes1
Has abstractyes

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