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Record W4361215767 · doi:10.3233/nre-220279

A national survey of evidence-based stroke rehabilitation intervention use in clinical practice among Canadian occupational therapists

2023· article· en· W4361215767 on OpenAlexaffabout
Amanda McIntyre, Ricardo Viana, Peiwen Cao, Shannon Janzen, Marcus Saikaley, Amber Harnett, Robert Teasell

Bibliographic record

VenueNeurorehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsSt Joseph's Health CareParkwood InstituteLawson Health Research InstituteWestern University
Fundersnot available
KeywordsPsychological interventionRehabilitationStroke (engine)MedicineOccupational therapyIntervention (counseling)Knowledge translationRandomized controlled trialPhysical therapyPhysical medicine and rehabilitationNursing

Abstract

fetched live from OpenAlex

BACKGROUND: More than 1,000 randomized controlled trials have been published examining the effectiveness of stroke rehabilitation interventions. OBJECTIVE: The objective of this study was to explore the use and non-use of evidence-based stroke rehabilitation interventions in clinical practice among Occupational Therapists across various stroke rehabilitation settings in Canada. METHODS: Participants were recruited from medical centres providing rehabilitation to stroke patients in each of the ten provinces across Canada (January-July 2021). Adult (18 + years) Occupational Therapists who provide direct rehabilitative care to individuals after a stroke completed a survey in either English or French. Therapists rated their awareness, use, and reasons for non-use of stroke rehabilitation interventions. RESULTS: 127 therapists (female = 89.8%), largely from Ontario or Quebec (62.2%) were included; most worked full-time (80.3%) in moderate-large (86.1%) cities. The greatest use of interventions were those applied to the body peripherally, without a technological component. Few individuals were aware of interventions applied to the brain (priming or stimulating) with a technological component, and they were rarely, if ever, used. CONCLUSION: Significant efforts should be made to increase the awareness of interventions which are supported by strong evidence through knowledge translation and implementation initiatives, particularly for those with a technological component.

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)0.138
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.138
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.209
GPT teacher head0.444
Teacher spread0.234 · 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 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

Citations3
Published2023
Admission routes2
Has abstractyes

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