Rehabilitation Care at the Time of Coronavirus Disease-19 (COVID-19) Pandemic: A Scoping Review of Health System Recommendations
Bibliographic record
Abstract
Purpose: The coronavirus disease-19 (COVID-19) was declared a pandemic by the World Health Organization in March 2020. COVID-19, caused by SARS-CoV-2 has imposed a significant burden on health care systems, economies, and social systems in many countries around the world. The provision of rehabilitation services for persons with active COVID-19 infection poses challenges to maintaining a safe environment for patients and treating providers. Materials and Methods: Established frameworks were used to guide the scoping review methodology. Medline, Embase, Pubmed, CINAHL databases from inception to August 1, 2020, and prominent rehabilitation organizations’ websites were searched. Study Selection: We included articles and reports if they were focused on rehabilitation related recommendations for COVID-19 patients, treating providers, or the general population. Data Extraction: Pairs of team members used a pre-tested data abstraction form to extract data from included full-text articles. The strength and the quality of the extracted recommendations were evaluated by two reviewers using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Results: We retrieved 6,468 citations, of which 2,086 were eligible for review, after duplicates were removed. We excluded 1,980 citations based on title and abstract screening. Of the screened full-text articles, we included all 106 studies. A summary of recommendations is presented. We assessed the overall evidence to be strong and of fair quality. Conclusion: The rehabilitation setting, and processes, logistics, and patient and healthcare provider precaution recommendations identified aim to reduce the spread of SARS-CoV-2 infection and ensure adequate and safe rehabilitation services, whether face-to-face or through teleservices. The COVID-19 pandemic is rapidly changing. Further updates will be needed over time in order to incorporate emerging best evidence into rehabilitation guidelines.
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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 | 0.044 | 0.180 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.034 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".