Exploring the perspectives of genetic counsellors and patients with limited English proficiency in genetic counselling sessions requiring interpretation services.
Bibliographic record
Abstract
Genetic counsellors hold unique positions in the health care field. Part of their role involves explaining genetic information to patients in a digestible way in order to empower them to make informed decisions about their genetic health. Medical interpreters are present during appointments with patients who have limited English proficiency (LEP). During these appointments, information is interpreted to the patient in their preferred language. Previous studies in the United States have explored the perspectives of genetic counsellors and interpreters in genetic counseling sessions with medical interpreters. There are currently no studies exploring these perspectives from a Canadian context. Additionally, there have been few studies exploring the point of view of the patients. Six genetic counsellors at the Health Sciences Center in Winnipeg, Manitoba and three patients with LEP were interviewed following genetic counselling appointments that required interpretation services. Semi-structured interviews were utilized to explore the participants’ perspectives, expectations, and experiences with medical interpretation. All interviews were transcribed and analyzed using thematic analysis to identify themes under a phenomenological framework. Five major themes were identified from our interviews: 1) Expectations of the providers, 2) Relationships and rapport building, 3) Mode of appointment delivery, 4) Flow of the genetic counselling session, and 5) Patients’ lived experiences. Overall, verbatim interpretation was preferred by all genetic counsellors and most patients. Additionally, genetic counsellors did not expect the medical interpreter to know genetic specific terminology and expressed that explaining these terms fell under their own domain and role. All participants preferred in-person to telephone appointments and pointed to visual cues as an important missing factor during phone appointments. Most genetic counsellors described having enough allocated time during appointments with medical interpreters so that all aspects of the genetic counselling appointment were addressed. Some genetic counsellors expressed wanting to learn more about the role of medical interpreters to better understand the medical interpreters’ expectations. Lastly, we identified and offered suggestions and recommendations for working with medical interpreters and patients with LEP.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".