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Record W4205480746 · doi:10.3138/ptc-2021-0020

A Survey of Canadian Physiotherapists’ and Physiotherapy Students’ Knowledge and Use of Executive Functioning Assessments in Clinical Practice

2022· article· en· W4205480746 on OpenAlexaffvenueabout
Nicole A. Guitar, Denise M. Connelly, Laura L. Murray, Susan Hunter

Bibliographic record

VenuePhysiotherapy Canada · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsPhysical therapyMedicineClinical PracticeCardiorespiratory fitness

Abstract

fetched live from OpenAlex

Purpose: This study examined what physiotherapists and physiotherapy students understand and know about executive functioning (EF), what EF outcome measures they use clinically, and whether their primary area of practice influences their assessment practices. Method: An open online survey was distributed to registered members of the Canadian Physiotherapy Association, its various divisions, and colleges of physiotherapy within Canada that took approximately 15 minutes to complete and was available for 13 months. Pearson correlation was used to assess the relationship between subjective and objective understanding and knowledge of EF (UKEF) and a one-way multivariate analysis of variance was used to analyze differences in survey responses based on respondents’ primary area of practice. Results: A total of 335 respondents consented to participate (completion rate = 78.4%). There was a significant moderate positive correlation between subjective and objective UKEF ( r = 0.43; 95% CI: 0.32, 0.54; n = 260; p < 0.001). Significant differences in survey responses were related to physiotherapists’ primary areas of practice (i.e., musculoskeletal, neurological, cardiorespiratory, or multi-systems; F 12,555.89 = 2.29, p = 0.008; Wilks Λ = 0.880; partial η 2 = 0.042). Conclusions: Respondents reported that they had good subjective UKEF, but this was only moderately correlated with objective UKEF.

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)0.003
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 score0.052
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.162
GPT teacher head0.548
Teacher spread0.386 · 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

Citations3
Published2022
Admission routes3
Has abstractyes

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