Communication practices with patients using a language other than French: a cross-sectional survey in a university hospital in France
Bibliographic record
Abstract
AIMS: This paper aims to shed light on routine communication practices between all types of hospital workers- medical, administrative and psycho-social -, and patients using a language other than French. METHODS: A cross-sectional survey was conducted at a University Hospital, located in a Parisian suburb, where the proportion of immigrants is high. The survey targeted any type of hospital employee, provided that the employee was in contact with patients. The survey items included: routine communication practices with patients using a language other than French; perceived quality of communication; issues experienced when communicating with non-French speaking patients; main languages raising communications difficulties; ways to improve communication with patients using a language other that French. Descriptive and bivariate analysis were conducted with R software. Survey findings were cross-analyzed with 2-year records of professional interpreter services at the University hospital. RESULTS: A total of 362 participants responded in June 2022 to the online survey, of which 353 had no missing value. All types of hospital staff were represented, the majority being paramedics and medical doctors. "The use of a professional interpreter" was ranked as third most used practice, behind "getting by" and "use of an accompanying adult". South Asian languages were those fueling the most important communication issues. Medical doctors and psychologists had significantly more access to professional interpreters, whereas paramedics and administrative staff made more use of application software. Several negative consequences on everyday care, significantly impacting its perceived quality, were raised. CONCLUSIONS: Our findings showed the importance of alleviating communication difficulties with patients using a language other than French, in order to achieve health equity, and means to achieve this are discussed.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".