Assessment of standing in Functional Capacity Evaluations: An exploration of methods used by a sample of occupational therapists
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".