Integrating genetic assistants into the workforce: An 18‐year productivity analysis and development of a staff mix planning tool
Bibliographic record
Abstract
In recent years, genetic (counseling) assistants have been integrated in the genetics workforce, such that one-third of genetic counselors now report working with a genetic assistant. While several studies showed that adoption of the genetic assistant model leads to an increase in patient volume, the impact of this role substitution has not been studied quantitatively beyond the cancer genetics workforce. This study utilized 18 years of data from a publicly funded genetics clinic with multiple specialties and varying staff mix. Time series regression modeling was applied to describe the evolving impact of genetic assistants on genetic counselor and clinical geneticist productivity (measured as patient volume). The regression models suggest that the integration of genetic assistants led to a sustainable increase in genetic counselor patient volume, while clinical geneticist patient volume was unaffected. Importantly, the models also demonstrated an interaction between the number of genetic counselors and genetic assistants, whereby the impact of adding a genetic counselor was greater as more genetic assistants were employed in the clinic, and vice versa. The main regression model was used to create "ClinMix: A Genetics Staff Mix Planning Tool," an Excel application that allows users to explore how different staffing plans could affect patient volume, by applying the parameters estimated from this data or their own. We hope this report and the ClinMix tool can be employed by the genetics workforce to advocate for further implementation and evaluation of genetic assistant positions. Adoption of the genetic assistant model may provide clinics the support needed to meet increasing service delivery demands and subsequently foster genetic counselor practice at "top of scope."
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".