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Record W2150855971 · doi:10.3233/wor-2011-1116

Assessment of standing in Functional Capacity Evaluations: An exploration of methods used by a sample of occupational therapists

2011· article· en· W2150855971 on OpenAlexaff
Elizabeth Gibson, Kryss McKenna, Marion Gray, Trish Wielandt

Bibliographic record

VenueWork · 2011
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsApplied psychologySample (material)Task (project management)PsychologyOccupational therapyEngineeringPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The primary aim of the study was to explore the current practice of occupational therapists when assessing standing performance during Functional Capacity Evaluations (FCEs). METHODS: A semi-structured interview was conducted with occupational therapists and the participants were interviewed using both open and closed questions. PARTICIPANTS: A sample of occupational therapists (n=20) from Queensland, Australia were involved in a survey. They were all experienced in conducting FCEs. RESULTS: Ninety percent of the respondents used a distracting task during the assessment of standing with standardised and non-standardised nuts and bolts assembly tasks the most commonly used. Respondents reported using a mix of biophysical, physiological and psychophysical clinical observations to assess standing. The nuts and bolts assembly activities used by the respondents were rated to be of low interest in terms of engaging the client. CONCLUSIONS: It was identified there are minimal guidelines in the literature which focuses on assessment of standing in FCEs. Questions were raised regarding the adequacy of the use of nuts and bolts activities as a suitably distracting task in FCEs and further research is required on assessing standing in functional capacity evaluation including the use of suitably distracting activities.

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)0.005
metaresearch head score (Gemma)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.734
GPT teacher head0.642
Teacher spread0.093 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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