Operational Considerations for Physical Therapy During COVID-19: A Rapid Review
Bibliographic record
Abstract
OBJECTIVE: Physical therapists play an important role in responding to pandemic and physical disaster situations. Existing literature can provide guidance to health care leadership teams to appropriately and safely leverage physical therapy resources and skill sets during the COVID-19 pandemic. The purpose of this study was to provide a review of the pandemic and physical disaster management literature to summarize physical therapy-specific operational considerations to assist hospital-based leadership teams in planning and response efforts during the COVID-19 pandemic. METHODS: A rapid review was conducted over a 4-week time frame (April-May 2020). The review team included 3 physical therapist clinician researchers, a health librarian, and a member of the physical therapy leadership team. The initial search strategy identified 303 articles, 80 of which were retained for full-text screening. Twenty articles were included in the review. RESULTS: Five main categories of operational considerations for physical therapy during the COVID-19 pandemic were identified: (1) organizational actions, (2) staffing considerations, (3) physical therapist roles, (4) physical resources, and (5) other considerations. Additional relevant information from physical therapists' experiences in physical disaster situations was also summarized. CONCLUSION: The evidence presented within this review can be used to inform facility-based and regional planning efforts during the current COVID-19 pandemic and in general preparedness planning. IMPACT: Physical therapists have an important role to play in response efforts related to major events that impact health and wellness, including the COVID-19 pandemic. Evidence-informed, facility-based, and regional planning during the current COVID-19 pandemic will help physical therapists enhance their role in treating patients in physical therapy and rehabilitation settings.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".