Physiotherapists’ and Physiotherapy Assistants’ Perspectives on Using Three Physical Function Measures in the Intensive Care Unit: A Mixed-Methods Study
Bibliographic record
Abstract
Purpose: We sought to understand physiotherapists’ and physiotherapist assistants’ perspectives on using three physical function measures in the intensive care unit (ICU) setting: the Activity Measure for Post-Acute Care Inpatient Mobility Short Form, the Johns Hopkins Highest Level of Mobility scale, and the Functional Status Score for the Intensive Care Unit. Method: A six-item questionnaire was developed and administered to physiotherapists and physiotherapist assistants working in adult ICUs at one U.S. teaching hospital. A single semi-structured focus group was conducted with seven physiotherapists, recruited using purposive sampling to include participants with a range of clinical experience. Results: Of 22 potential participants, 18 physiotherapists and 2 physiotherapist assistants completed the questionnaire. Seven physiotherapists participated in the focus group. The questionnaire found favourable perspectives on the use of the three physical function measures in clinical practice, and the focus group identified five themes related to clinicians’ experience with using them: (1) ease of scoring, (2) usefulness in inter-professional communication, (3) general ease of use, (4) responsiveness to change in physical function, and (5) generalizability across patients. Conclusions: The most frequently discussed themes in this study were ease of scoring and usefulness in inter-professional communication, highlighting their importance in designing and selecting physical function measures for clinical use in the ICU setting.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".