Telework and telerehabilitation programs for workers with a stroke during the COVID-19 pandemic: A commentary
Bibliographic record
Abstract
BACKGROUND: Due to the coronavirus disease 2019 (COVID-19) pandemic, rehabilitation facilities have become less accessible for patients with a stroke. Lack of early, intensive rehabilitation misses the opportunity for recovery during the critical time window of endogenous plasticity and improvement post-stroke. OBJECTIVES: The purpose of this commentary was to highlighting the benefits of telework and telerehabilitation programs for workers with a stroke during the COVID-19 pandemic. METHODS: Relevant publications regarding the management of individuals with a stroke, telerehabilitation and teleworking in the setting of COVID-19 were reviewed. RESULTS: Previous studies showed that telerehabilitation can effectively provide an alternate method of promoting recovery for patients with a stroke. With the physical distancing precautions in place for mitigating viral spread, teleworking can also provide a method for long term recovery and improvements in quality of life after a stroke. CONCLUSIONS: Overall, this commentary addresses the benefits of physically distant, safe and effective alternatives to support individuals who live with a stroke during COVID-19 pandemic.
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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 | 0.008 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.029 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".