Factors Influencing Physical Therapists’ Rehabilitation Prescription in the ICU: Semi-Structured Interviews with Qualitative Analysis
Bibliographic record
Abstract
Purpose: Despite a plethora of studies on early rehabilitation, specific guidelines for rehabilitation prescription parameters are lacking. The objective of this study was to evaluate how physical therapists determine rehabilitation parameters, such as initiation, frequency, intensity, duration, and type of interventions for patients in the ICU. Method: Semi-structured interviews were conducted between April and August of 2021 using video conferencing software and following a written interview guide. Purposive sampling was used among interested physical therapists to select those who work across a variety of ICU types with a range of years of ICU experience. We used thematic analysis to identify emerging themes using an inductive approach. Results: We interviewed 30 physical therapists in the United States with 14 (47%) and 16 (53%) having ≤5 years and >5 years of ICU clinical experience, respectively. Nine factors were identified as impacting all rehabilitation prescription parameters (e.g., medical appropriateness, diagnosis/prognosis, and alertness/sedation). For decisions about each parameter there were a set of factors identified: five for initiation (e.g., indication for physical therapy; ventilator settings/oxygen), four for frequency (e.g., baseline function; prior therapy session), three for intensity (e.g., patient appearance and subjective response), nine for duration (e.g., session preparation; quality of performance), and eight for type of intervention (e.g., progressive mobility; patient goals). Conclusions: Interviews examining rehabilitation parameters, revealed that physical therapists consider each of these simultaneously when making decisions about rehabilitation prescription. Furthermore, physical therapists appear to modify to the intervention not only based on patient progress but also by other external factors related to working in an ICU environment (e.g., equipment availability, interruptions for other medical procedures).
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".