International genetic counseling: What do genetic counselors actually do?
Bibliographic record
Abstract
We conducted an exploratory survey of genetic counselors internationally to assess similarities and differences in reported practice activities. Between November 2018 and January 2020 we conducted a mass emailing to an estimated 5600 genetic counselors in different countries and regions. We obtained 189 useable responses representing 22 countries, which are included in an aggregate manner. Data from countries with 10 or more responses, comprising 82% of the total (N = 156), are the primary focus of this report: Australia (13), Canada (26), USA (59), UK (17), France (12), Japan (19) and India (10). Twenty activities were identified as common (≥74%) across these countries, encompassing most subcategories of genetic counseling activity. Activities with most frequent endorsement include: reviewing referrals and medical records and identifying genetic testing options as part of case preparation; taking family and medical histories; performing and sharing risk assessment; and educating clients about basic genetic information, test options, outcomes and implications, including management recommendations on the basis of the test results. Genetic counselors also consistently establish rapport, tailor the educational process, facilitate informed decision making and recognize factors that may impact the counseling interaction. The least endorsed activities were in the Medical History category. Notable differences between countries were observed in the endorsement of 33 activities, primarily in the Contracting and Establishing Rapport, Family History, Medical History, Assessing Patients Psychosocially and Providing Psychosocial Support categories. Generalizations about international practice patterns are limited by the low response rate. However, this study is, to our knowledge, the first to systematically compare the clinical practice and specific activities of genetic counselors working in different countries.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".