Genetic counseling research and COVID‐19: A lesson in resiliency
Bibliographic record
Abstract
GenCOUNSEL is the largest genetic counseling research grant awarded to date and brings together experts in genetic counseling, genomics, law and policy, health services implementation, and health economics research. It is the first project of its kind to examine the genetic counseling issues associated with the clinical implementation of genome-wide sequencing (exome and genome sequencing). GenCOUNSEL is a Canadian-based, multi-method research study that takes place over a variety of sites, including non-clinical, clinical, and laboratory research sites and includes the training of undergraduate and graduate students. The COVID-19 pandemic will likely have a lasting impact on genetic counseling service delivery, research, and training. Almost every aspect of the GenCOUNSEL research project has been impacted by the COVID-19 pandemic. Here we describe how our research recruitment strategies, methods, resource allocation, and training capacity have been affected. We discuss ways that we have adapted to the pandemic including revision of our research methods and work to understand the barriers in order to optimize opportunities. We finish with take-home messages to fellow researchers highlighting the importance of resiliency in genetic counseling research.
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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 | 0.147 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.018 | 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".