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Record W2069498140 · doi:10.5014/ajot.2012.003475

Effect of Imagery Perspective on Occupational Performance After Stroke: A Randomized Controlled Trial

2012· article· en· W2069498140 on OpenAlexaboutno aff
Dawn M. Nilsen, Glen Gillen, Theresa DiRusso, Andrew M. Gordon

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2012
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialPerspective (graphical)Occupational therapyStroke (engine)Physical medicine and rehabilitationPsychologyPhysical therapyGuided imageryMedicinePsychiatryComputer scienceArtificial intelligenceInternal medicineEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: This preliminary study sought to determine whether the imagery perspective used during mental practice (MP) differentially influenced performance outcomes after stroke. METHOD: Nineteen participants with unilateral subacute stroke (9 men and 10 women, ages 28-77) were randomly allocated to one of three groups. All groups received 30-min occupational therapy sessions 2×/wk for 6 wk. Experimental groups received MP training in functional tasks using either an internal or an external perspective; the control group received relaxation imagery training. Participants were pre- and posttested using the Fugl-Meyer Motor Assessment (FMA), the Jebsen-Taylor Test of Hand Function (JTTHF), and the Canadian Occupational Performance Measure (COPM). RESULTS: At posttest, the internal and external experimental groups showed statistically similar improvements on the FMA and JTTHF (p < .05). All groups improved on the COPM (p < .05). CONCLUSION: MP combined with occupational therapy improves upper-extremity recovery after stroke. MP does not appear to enhance self-perception of performance. This preliminary study suggests that imagery perspective may not be an important variable in MP interventions.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.391
Teacher spread0.370 · 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 designRandomized trial
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

Citations60
Published2012
Admission routes1
Has abstractyes

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