Rehabilitation at the Time of Pandemic: Patient Journey Recommendations
Bibliographic record
Abstract
Purpose: The World Health Organization (WHO) declared severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a pandemic in March 2020, causing almost 3.5 million coronavirus disease (COVID-19) related deaths worldwide. The COVID-19 pandemic has imposed a significant burden on healthcare systems, economies, and social systems in many countries around the world. The access and delivery of rehabilitation care were severely disrupted, and patients have faced several challenges during the COVID-19 outbreak. These challenges include addressing new functional impairments faced by survivors of COVID-19 and infection prevention to avoid the virus spread to healthcare workers and other patients not infected with COVID-19. In this scoping review, we aim to develop rehabilitation recommendations during the COVID-19 pandemic across the continuum of rehabilitation care. 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 recommendations for COVID-19 survivors or the general population at the time of the COVID-19 pandemic. Data Extraction: Two of our team members used the pre-tested data extraction 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 GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach. Results: We retrieved 6,468 citations, of which 2,086 were eligible after removing duplicates. We excluded 1,980 citations based on the title and the abstract. Of the screened full-text articles, we included 106 studies. We present recommendations based on the patient journey at the time of the pandemic. We assessed the evidence to be of overall fair quality and strong for the recommendations. Conclusion: We have combined the latest research results and accumulated expert opinions on rehabilitation to develop acute and post-acute rehabilitation recommendations in response to the global COVID-19 pandemic. Further updates are warranted in order to incorporate the emerging 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 | 0.058 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".