Experiences of genetic counselors practicing in multiple languages: Progress and places for improvement
Bibliographic record
Abstract
As awareness of the value of genetic counseling services increases, there has been greater recognition of the need to diversify service delivery into different languages. Studies within genetic counseling and related fields have identified complications that can arise from language nonconcordance between provider and patient. A strategy to mitigate language barriers is prioritizing the development of a multilingual workforce of genetic counselors (GCs) who can communicate with patients in their preferred language. This exploratory study assessed the experiences of multilingual GCs who have practiced in a clinical role with the aim to identify relevant challenges and differences when counseling in their nondominant language. Statistical analysis was performed to identify differences in session tasks and emotions experienced when counseling in one's nondominant language versus their dominant language. Data analysis identified an increase in reported difficulty level for most clinical tasks while using a nondominant language, most notably for difficulty with psychosocial counseling, disclosing results, and administrative tasks. Participants were also surveyed on employer support and resources provided. Overall, results suggest that multilingual GCs may benefit from greater support in certain areas within clinical roles to enhance their ability to provide patient care in their nondominant language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".