The content of talk about health conditions and medications during appointments involving interpreters
Bibliographic record
Abstract
INTRODUCTION: Interpreters often join immigrants and physicians to permit communication. OBJECTIVE: To describe the content of talk about health problems and medications during clinical encounters involving interpreters [professionals (PI) or family members (FI)]. METHODS: We analysed one regularly scheduled encounter for each of 16 adult patients with his family physician and their usual interpreter (10 with a PI and 6 with a FI). A different PI, not involved in the consultations, translated the non-English or French parts. We coded all utterances about each medical problem and each medication using six health problem and 16 medication topics from MEDICODE, a validated coding scheme. RESULTS: Physicians and patients addressed an average of 3.6 problems and 3 medications per encounter. No psychosocial problems were discussed in encounters involving FIs. On average, three topics were discussed per problem. In order of frequency, they were follow-up, explanations of the condition, non-drug management, consequences, self-management and emotions about the problem. Encounters involving PIs were more likely than encounters with FIs to include discussions of emotions about the problem (42% versus 4%, P = 0.001) and indications for follow-up (88% versus 28%, P < 0.001). An average of 6.5 topics was discussed per medication. Commonest topics discussed were medication class, how the drug was being used, achieved effect and expected effect. CONCLUSIONS: One can address multiple problems and share vital information even in the presence of a language barrier. When FIs are interpreting, physicians would do well to make a particular effort to bring the patient's psychological and emotional issues into the interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".