Communicative hurdles in multilingual interpreter-mediated consultations: what trainee data teach us
Bibliographic record
Abstract
This paper reports on an interprofessional training initiative for medical students and trainee interpreters. Relying on a mixed-method analysis of 10 video-recorded simulated consultations at Ghent University (Belgium), the paper investigates which factors impact on trainee interpreters’ (in)complete rendition of medical students’ communicative goals. Our analysis reveals that incomplete renditions can be attributed to (i) long turns including more than one communicative goal; (ii) long turns including large chunks of information; and (iii) a combination of old (i.e. previously given) and new information. Based upon these results, we formulate the following recommendations for the interprofessional training of trainee interpreters: (i) familiarise students with the Calgary Cambridge guide (ii) prepare them for the interpretation of longer turns (iii) teach them how to interrupt a doctor as a safeguard against inaccurate interpretation and (iv) instruct them how to find the right balance between inserting old and new information.
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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 | 0.026 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".