The Structure of Clinical Consultation: A Case of Non-Native Speakers of English as Participants
Bibliographic record
Abstract
BACKGROUND: In many parts of the world, patients may find it difficult to visit doctors who share the same language and culture due to the intermingling of people and international recruitment of doctors among many other reasons. In these multilingual multicultural settings (MMSs), doctor-patient interactions face new communication challenges. This study aims to identify the structure of clinical consultation and its phases in an MMS where both doctors and patients are non-native speakers (NNSs) of English. METHOD: This study takes on a discourse analytic approach to examine the structure of clinical consultation as an activity type. 25 clinical consultation sessions between non-native speakers of English in a public healthcare centre in Malaysia were audio-recorded. FINDINGS & DISCUSSION: The results show that there are some deviations from the mainstream structure of clinical consultations although, in general, the pattern is compatible with previous studies. Deviations are particularly marked in the opening and closing phases of consultation. CONCLUSION: In almost all interactions, there is a straightforward manner of beginning medical consultations. The absence of greetings may have naturally reduced the length of talk. Hence, by directly entering medical talks, the doctors voice their concern on the curing aspects of the consultation rather than its caring facets. The preference of curing priority to caring is more goal-oriented and in alignment with the consultation as an activity type.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".