Refining the activities of genetic assistants: Development of task statements applicable across practice settings
Bibliographic record
Abstract
Although genetic (counseling) assistants (GAs) have been implemented in many institutions, their roles vary widely. Therefore, this study aimed to refine our knowledge of GA tasks across work settings and specialties. Tasks performed by GAs were extracted from peer-reviewed articles, publicly available theses, and job postings, then analyzed using directed content analysis. Briefly, task statements were coded using broad categories from previous studies, with new categories added as emergent. Coded tasks were combined and condensed to produce a final task list, which was reviewed by subject matter experts. Sixty-one task statements were extracted from previous studies and 335 task statements were extracted from job descriptions. Directed content analysis produced a list of 40 unique tasks under 10 categories (8 from original research and 2 from the data). This study design resulted in a refined list of GA tasks that may be applicable across work settings and specialties, which is an essential step towards defining the scope of GA work. Beyond the human resource applications of the refined task list, this work may also benefit genetics services by reducing role overlap, improving efficiencies, improving employee satisfaction, and informing the development/improvement of training and other educational materials.
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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 | 0.060 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".