How Has COVID-19 Changed the Way We Do Virtual Care? A Scoping Review Protocol
Bibliographic record
Abstract
The coronavirus disease (COVID-19) pandemic created worldwide interest and use of virtual care to support public health measures and reduce the spread of infection. While some forms of virtual care have been used prior to COVID-19 such as telemedicine, little is known about other virtual modalities such as video conferencing, wearables and other digital technologies. The COVID-19 pandemic has presented an opportunity to question the efficacy and safety of virtual care, especially in terms of patient outcomes among those self-isolating. The purpose of this scoping review is to examine the safety of virtual care among active COVID-19 patients in the community and examine the types and dose of virtual care. Finally, this review will examine what patient outcomes are identified from interventions delivered virtually to treat COVID-19. We followed a systematic process guided by the PRISMA checklist for scoping reviews with a comprehensive search strategy across four bibliographic databases and handsearching reference lists. We undertook a blinded, two-stage screening process with eligibility criteria. All citations and screening were managed using the DistillerSR software. Data were extracted using a data extraction tool developed for this project. The conclusions from this review will offer greater understanding for how virtual care can be used among community-based COVID-19 patients.
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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 | 0.104 | 0.102 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.065 | 0.012 |
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".