How Inclusion of Genetic Counselors on the Research Team Can Benefit Translational Science
Bibliographic record
Abstract
Translational research in medicine is necessarily a team-based endeavor, and, indeed, translational research teams often include researchers from many different disciplines. We outline some of the practical challenges that are particularly salient to translational research, both for scientists and study participants, and propose that genetic counselors--a group of specialty-trained health care professionals who are as yet only infrequently recruited to collaborate in translational research teams--could contribute a unique perspective and skill set that would be invaluable in the effective navigation of these challenges. We propose that collaboration with genetic counselors could not only benefit individual translational research teams but also potentially help shift the research agenda for translational medicine.
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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 | 0.218 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".