On equal footing? The impact of patient companions on Lifeworld integration and patient-centeredness in linguistically diverse emergency consultations
Bibliographic record
Abstract
INTRODUCTION: Patient companions (PCs) can have an ambiguous impact on the quality of communication during multilingual medical consultations and therefore on health outcomes. Studies of multilingual medical consultations have focused mainly on PCs' role as interpreters, with less regard to other roles they may take up. This paper uses mixed methods to investigate PC role dynamics in multilingual PC-mediated consultations and how they affect the management of a patient's Lifeworld, a crucial element for history taking and rapport building. METHODS: Nine recordings of multilingual PC-mediated consultations from a Brussels emergency department, complemented with ethnographic notes and clinician interviews, were subjected to linguistic-ethnographic analysis and a codification of communication patterns and PC roles to explore the link between PC roles and Lifeworld management. PC roles were grouped into four stances (Linguistic agent, Lifeworld agent, System agent, and Principal). The communication patterns were grouped into three categories ("strictly medicine", "Lifeworld heard/included", and "Lifeworld interrupted"). RESULTS: In ED consultations, patients' Lifeworld frequently remains inadequately addressed, primarily due to physician interruptions. Significant associations are observed between roles taken up by PCs and the way patients' Lifeworld is managed. Successful integration of Lifeworld aspects is best supported by PCs taking up the role of System agent, possibly because it allows them to link the Lifeworld directly to medical issues. Linguistic-ethnographic analysis reveals how language barriers, PC role changes and Lifeworld management strategies are taking shape organically (often implicitly) determined by a wide range of situational factors, such as the complexity of the topic, the available (non)verbal linguistic repertoire, the level of meta-communication, explicit role negotiation and timing. CONCLUSION: Since poor Lifeworld management negatively impacts care, clinicians should be trained to detect and manage role dynamics and relevant situational factors in PC-mediated multilingual consultations to protect patients' right to high-quality communication and healthcare.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".