Bilingual health communicators:role delineation issues
Bibliographic record
Abstract
Managers of health care services are seeking new opportunities to improve communication with clients who have limited English proficiency. An increase in bilingual health staff and the frequent use of their language skills in patient encounters provides opportunities but also brings with it confusion surrounding the role of interpreters and bilingual health staff. Secondary analysis of transcripts from 18 focus groups with monolingual and bilingual health staff has provided a method of distinguishing the roles of these complementary communicators. This paper clarifies the roles of interpreters and bilingual communication facilitators using seven key features: scope of language, language proficiency, nature of communication/interaction, nature of the contact and relationship, client responsibilities, and relationship with other health care providers. We discuss differences in how bilingual health staff use language when providing care, and alternative types of interactions interpreters could adopt to extend their current role. A collaborative group of communicators located within a health team is proposed, that is able to identify need and select the best communicator for the task.
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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 | 0.106 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".