A new approach to digital health? Virtual COVID-19 care: A scoping review
Bibliographic record
Abstract
Aims: The use of virtual care enabled by digital technologies has increased, prompted by public health restrictions in response to COVID-19. Non-hospitalized persons in the acute phase of COVID-19 illness may have unique health needs while self-isolating in the community. This scoping review aimed to explore the nature of care, the use of digital technologies, and patient outcomes arising from virtual care among community-based self-isolating COVID-19 patients. Methods: Literature searches for peer-reviewed articles were conducted in four bibliographic databases: CINAHL, Medline, Embase and Cochrane Database of Systematic Reviews between January and February 2022, followed by hand-searching reference lists of included articles. Two levels of screening using defined eligibility criteria among two independent reviewers were completed. Results: Of the 773 articles retrieved, 19 were included. Results indicate that virtual care can be safe while enabling timely detection of clinical deterioration to improve the illness trajectory. COVID-19 virtual care was delivered by single health professionals or by multidisciplinary teams using a range of low-technology methods such as telephone to higher technology methods like wearable technology that transmitted physiological data to the care teams for real-time or asynchronous monitoring. Conclusion: The review described the varied nature of virtual care including its design, implementation, and evaluation. Further research is needed for continued exploration on how to leverage digital health assets for the delivery of appropriate and safe virtual COVID-19 community care, which can support patient recovery, control transmission, and prevent intensifying the burden on the health care system, especially during surges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".