Genetic counselling resources in non-english languages: A scoping review
Bibliographic record
Abstract
Objective: Genetic counselling is essential for individuals seeking genetic or genomic testing. Whereas innovative strategies for GC delivery are being explored to meet the growing demand on the clinical genetics workforce, it is essential to consider the unique needs of culturally and linguistically diverse populations. Methods: We conducted a scoping review to examine the extent, range, and gaps in the body of non-English, patient-facing educational resources available for Limited English Proficient (LEP) patients accessing clinical genetics and genomics services. Results: The literature search returned 246 unique resources, most available in several languages. Forty-six languages were represented, with Spanish, Russian, and French being the most common. Resources were in various formats and were of varying quality. Conclusions: There is a lack of high-quality supplementary genetics education material available in languages other than English, which limits the quality-of-care that LEP families may receive compared to their English-speaking counterparts. Of equal concern is the difficulty in finding existing resources and in determining their quality. Innovation: This research highlights the important need for genetics education material that is of good quality in languages other than English and the challenges associated with identifying this material. A central, curated repository, perhaps sponsored by a genetic counselling organization, would be of great benefit to help genetic counsellors meet the needs of their culturally and linguistically diverse patients.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".