English for the Workplace: Doing Patient-Centred Care in Medical Communication
Bibliographic record
Abstract
Canada, like other first-world countries, relies in large part on professional im- migrants trained in other cultures and languages to complement its workforce in a wide range of professions, including medicine. International medical graduates (IMGs) who are nonnative English-speaking (NNES) and who have trained in different medical contexts are often unfamiliar with the sociopragmatic norms underlying both general communication and medical practice in their new host countries, and as a result they can have difficulty using the pragmalinguistic resources needed to strike the appropriate interpersonal note in patient-centred approaches to communication. In this article we used data collected through role-plays performed in an Australian setting by practicing, locally trained, native English-speaking (NES) doctors and NNES IMGs to identify the features of patient-centred medical communication that the latter can find challenging. This approach allowed us to use the discourse to highlight those features of approachability that are likely to be relevant to immigrant professionals in both Canada and Australia. It also helped us to illustrate how discourse data can be used to identify culturally appropriate ways of communicating that can, in turn, contribute to an accurate evidence base from which culturally appropriate communication courses for IMGs and other professionals may be developed.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".