Analysis of the reported use of practice‐based competencies by North American genetic counselors during the COVID‐19 pandemic
Bibliographic record
Abstract
Genetic counseling services changed due to the COVID-19 pandemic. Many genetic counselors (GCs) moved from in-person to telehealth services. Others were redeployed by choice or necessity, using their expertise to provide COVID-19 care and education. For some, their employment status changed due to budgetary constraints or decreasing referrals. This study surveyed North American GCs to assess the relative use of genetic counseling Practice-Based Competencies (PBCs) as a proxy for the skills used during the first wave of the pandemic, whether GCs were in their current role or in new or adjusted roles. A secondary aim was to determine whether GCs believe their training should be refocused in view of the workforce shifts posed by the pandemic. The survey comprised closed- and open-ended questions and was completed in full by 97 respondents. The study population was representative of the general genetic counseling workforce in terms of gender, race/ethnicity, age, and practice area when compared to the National Society of Genetic Counselors 2020 Professional Status Survey. Most participants (97.9%) indicated that the COVID-19 pandemic resulted in a change to their work, and 89.7% used at least one PBC at a different frequency than before the pandemic. The most significant change was the adaptation of genetic counseling skills for varied service delivery models: 83.5% of respondents indicated that their roles and responsibilities moved to a remote setting and/or utilized telehealth. The majority of participants felt competent using the PBCs during the pandemic. Major themes that emerged from the qualitative data were as follows: (a) adaptation of service delivery, (b) translation of genetic counseling skills, and (c) provision of psychosocial support. This study highlights practice changes for GCs due to the COVID-19 pandemic as well as the increased use of, and need for focused training in, varied service delivery models.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".