Characterization of genetic counselor practices in inpatient care settings
Bibliographic record
Abstract
Rapid genomic testing is increasingly used in inpatient settings for diagnostic and treatment purposes. With the expansion of genetic testing in this setting, requests for inpatient genetics consultations have increased. There have been reports of genetic counselors working in inpatient care, though their specific roles are not well described. In this study, we characterized the roles of genetic counselors practicing in inpatient care settings in the United States and Canada. Genetic counselors were recruited via professional organization listservs to complete an online survey. The survey gathered information on participants' roles and workflow of inpatient genetics consultation services at their institution. Responses from 132 participants demonstrate that 50.4% of genetic counselors cover genetics consultations as needed or on a rotating schedule (34.6%). They practice in general pediatric (59.1%), neonatal (42.5%), cancer (28.3%), and/or prenatal (18.9%) specialties, among others. Participants reported working independently (16.1%) or with other providers (54.8%), including geneticists and other attending physicians. The workflow of genetics consultation services varies between institutions in the delivery of consults, members of the inpatient genetics consultation care team, and administrative support. Fifty percent of participants reported having no exposure to inpatients during graduate training, and 87.3% of participants reported receiving no institutional training for their inpatient role. This is the first study to describe roles of genetic counselors in inpatient care. It establishes a foundation for future research on inpatient genetic counseling and genetic counseling outcomes in inpatient services. As demand for genetics expertise in inpatient care grows, genetic counselors can be hired to serve inpatient populations alongside genetics and non-genetics providers.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".