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Record W2325052133 · doi:10.2340/16501977-0820

Physical therapists’ perceptions and use of standardized assessments of walking ability post-stroke

2011· article· en· W2325052133 on OpenAlexafffundabout
Nancy M. Salbach, Susan Jaglal

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPhysical therapyStroke (engine)Physical medicine and rehabilitationMedicineTest (biology)Psychological interventionMEDLINEStandardized testFunctional Independence MeasureGaitRehabilitationPsychologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine physical therapists' perceptions and use of standardized assessments of walking ability post-stroke. DESIGN: Cross-sectional survey. METHODS: A questionnaire was posted to physical therapists in neurological practice registered in Ontario, Canada (n = 1155). Of the 705 responders, 270 treated adults with stroke and completed the questionnaire. RESULTS: Assessment tools most frequently used with > 6/10 patients were the Chedoke-McMaster Stroke Assessment (61.1%), Functional Independence Measure (45.2%), and gait speed test (32.2%). Only 11.1% consistently used the 6-minute walk test. The tools were used to evaluate (44.6%), monitor change over time (42.9%), form a prognosis (19.4%) or judge readiness for discharge (28.4%). Some therapists (40.1%) were unaware or unsure that valid and reliable measures of walking exist. As many as 80.5% of respondents agreed or strongly agreed that clinical practice guidelines should recommend specific measures of walking ability for use post-stroke. CONCLUSION: A moderate number of physical therapists consistently use standardized assessment tools to evaluate or monitor change in walking limitation post-stroke. Interventions to improve use must increase awareness, in addition to the perceived relevance and applicability, of recommended assessment tools.

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)0.004
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 score0.475
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.030
GPT teacher head0.344
Teacher spread0.315 · 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

Citations77
Published2011
Admission routes3
Has abstractyes

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