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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".