COVID‐19 and the adaptive evolution of genetic counseling
Bibliographic record
Abstract
Emerging diseases such as the Coronavirus Disease (COVID-19) have exposed severe weaknesses in the United States and global health. Healthcare systems have struggled and are still severely challenged and strained by this pandemic. It is clear that additional resources are needed to support healthcare providers in managing this and future pandemics. Genetic counselors can play an important supporting role in this fragile ecosystem because their comprehensive and broad training makes them uniquely qualified to meet many of the challenges that arise when healthcare workers and patients are faced with novel diseases. This paper describes the recent involvement of a telegenetic counseling company (Metis Genetics) in communicating and explaining COVID-19 serum antibody results to patients and physicians. This experience demonstrates how genetic counselors may be called upon to play a vital supporting role in the management of infectious disease pandemics. From May 2020 to July 2020, our genetic counseling telegenetics team was asked to provide support to more than 1,580 patients who underwent serum COVID-19 antibody testing and to educate their healthcare providers on the performance properties of this new test. The genetic counselors were able to utilize their expertise to convey test results, information on Center for Disease Control and Prevention (CDC) recommendations, COVID-19 fact-based evidence, to provide psychological support and reassurance to patients, and to respond to providers questions about the test. This experience suggests that the genetic counselors' skillset that has allowed the profession to continuously evolve can also be used in the management of pandemics by communicating directly with the public, supporting other healthcare workers, and assisting individual patients and families navigate the many medical and psychological issues caused by such events.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".