Telehealth as a Pandemic Silver Lining: Healthcare Lessons from COVID-19
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third coronavirus to emerge in the past decade after the 2010 SARS-CoV and 2012 MERS-CoV, which originated in China and Saudi Arabia, respectively. The virus is transmitted via small droplets which are produced during activities such as coughing, sneezing, and talking and spread through close human contact or touching infected surfaces. Since its first reported case in Wuhan, China in December 2020, the virus has proved to be highly infectious, reaching epidemic levels with about 2.8 million COVID-19 cases recorded globally. As a result, the World Health Organization was prompted to declare it as a public health emergency of international concern. The virus is of unknown aetiology and has no clinical countermeasures to date; therefore, prevention is the best strategy to prevent its spread. Many countries have enforced physical distancing, banned public gathering, and restricted mobility and transportation options. However, such preventive measures have side-effects which negatively impact healthcare and population health at various levels. Physicians and nurses treating COVID-19 patients are often required to be isolated from their family. Further, clinicians who are not well-versed in the complexities and risks of infectious diseases are facing new challenges. Patients requiring regular or urgent care (e.g., expectant mothers and patients awaiting elective/ emergency surgery) are experiencing limited access to care. Telehealth can ameliorate some of these side-effects and improve healthcare access along with the quality of life for both patients and practitioners.
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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 | 0.012 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".