Roles of community interpreters in pediatrics as seen by interpreters, physicians and researchers
Bibliographic record
Abstract
This paper is an attempt at defining more clearly the various roles of community interpreters and the processes implicitly connected with each of them. While the role of the interpreter is a subject that has been widely discussed in the social science literature, it is less present in the biomedical one, which tends to emphasize the importance of interpreting in overcoming language barriers, rather than as a means of building bridges between patients and physicians. Hence, studies looking at interpreted medical interactions suggest that the presence of an interpreter is more beneficial to the healthcare providers than to the patient. This statement is illustrated by the results of a recent study in a pediatric outpatient clinic in Switzerland. It is suggested that, in the consultations, interpreters act mainly as linguistic agents and health system agents and rarely as community agents. This is consistent with the pediatricians’ view of the interpreter as mainly a translating machine. A new typology of the varying roles of the interpreter is proposed, outlining the relation to cultural differences maintained therein. Some recommendations for the training of interpreters and healthcare providers are suggested.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".