Medical French and Medical English: General Language Skills for a Bilingual Country/Langage médical francophone et anglophone: habiletés de communication médicales attendues dans un pays bilingue
Bibliographic record
Abstract
It has been argued that Canada contains two solitudes, based primarily upon its European founding languages: English and French. 1 , 2 Bringing these solitudes closer requires effort and empathy. It also requires common words and phrases. This is the goal of this modest language primer for acute care practitioners. Many medical practitioners treat language-discordant patients and families (both within Canada and worldwide). Interpreters can be invaluable, and can avoid the loss of confidentiality that occurs if we rely upon family members. However, the unusual hours and time pressures of acute care medicine mean that we cannot assume translators will always be available. Moreover, patients who use translators are often less satisfied with their care, 3 may be less informed when providing consent, 4 and may demonstrate less outpatient compliance. 5 Therefore, it is important that front-line practitioners have basic language skills. Empathy is integral to patient-focused care. Canadian 6 and American 7 data suggest that when we cannot communicate in a patient’s native language, we treat that patient differently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".