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Record W4224318126 · doi:10.1177/15394492221087968

Occupational Therapy Interventions for Poststroke Fatigue: A Scoping Review

2022· review· en· W4224318126 on OpenAlexaff
Tatyana Smetheram, Maria Emilia Amiama, Debbie Hébert, Geoff Law, Deirdre Dawson

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2022
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsBaycrest HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionPsychoeducationMindfulnessMedicineOccupational therapyPhysical therapyStroke (engine)Clinical psychologyPhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Fatigue after stroke can negatively affect the survivors' well-being. Despite the high prevalence and consequences of poststroke fatigue (PSF), there is no specific guidance for occupational therapy practitioners (OTPs) to address this symptom. OBJECTIVES: The objectives of the study were to identify and describe the research on potential occupational therapy (OT) interventions for PSF. METHOD: Three databases were searched using scoping review methodology. Two authors completed a title and abstract and full-text review. Study characteristics, participant characteristics, qualities of interventions, and outcome measures were extracted and synthesized. RESULTS: Eight studies met selection criteria. Studies were conducted with stroke and traumatic brain injury patients in outpatient, inpatient, and community settings. Interventions included psychoeducation and behavior change, multicomponent programs, and mindfulness-based stress reduction therapies. The Fatigue Severity Scale and the Mental Fatigue Scale were commonly used. CONCLUSION: Evidence for OT interventions targeting PSF is limited. Recommendations for future research are provided.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.641
GPT teacher head0.629
Teacher spread0.013 · 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 designOther design
Domainnot available
GenreReview

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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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