Expanding the primary care workforce by integrating genetic counselors in multidisciplinary care teams
Bibliographic record
Abstract
Collectively, rare diseases are common, affecting approximately 8% of the population in Canada and the USA. Therefore, the majority of primary care (PC) clinicians will care for patients who are affected or at risk for a genetic disease. Considering the increasing ways in which genetics is being implemented into all areas of healthcare, one way to address these needs and expand the capacity of the PC workforce is through the integration of genetic counselors (GCs) into PC multidisciplinary teams. GCs are Masters-educated allied health professionals with specialized training in molecular genetics, communication, and short-term psychotherapeutic counseling. The current models of GCs in PC mimic other multidisciplinary models. Complex tasks related to genetics, such as pre- and post-test counseling, genetic test selection, and results interpretation, are conducted by GCs, which, in turn, allows physicians, nurse practitioners, and other PC providers to work at the top of their scope of practice. Quality genetics services provided by GCs improve clinical outcomes for patients and their families; the simultaneous provision of genetic education and psychological support by a GC is associated with an increase in patient knowledge, perceived personal control, decrease in distress, and can lead to positive health behavior changes, all of which are aligned with the goals of primary healthcare. With their extensive training in clinical care, medical communication, and psychotherapeutic counseling, integrating GCs into PC care teams will improve the care patients receive and allow PC clinicians to ensure their patients are at the forefront of the personalized medicine revolution.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".