Patient–physician language concordance and quality and safety outcomes among frail home care recipients admitted to hospital in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: When patients and physicians speak the same language, it may improve the quality and safety of care delivered. We sought to determine whether patient-physician language concordance is associated with in-hospital and postdischarge outcomes among home care recipients who were admitted to hospital. METHODS: We conducted a population-based study of a retrospective cohort of 189 690 home care recipients who were admitted to hospital in Ontario, Canada, between 2010 and 2018. We defined patient language (obtained from home care assessments) as English (Anglophone), French (Francophone) or other (allophone). We obtained physician language from the College of Physicians and Surgeons of Ontario. We defined hospital admissions as language concordant when patients received more than 50% of their care from physicians who spoke the patients' primary language. We identified in-hospital (adverse events, length of stay, death) and post-discharge outcomes (emergency department visits, readmissions, death within 30 days of discharge). We used regression analyses to estimate the adjusted rate of mean and the adjusted odds ratio (OR) of each outcome, stratified by patient language, to assess the impact of language-concordant care within each linguistic group. RESULTS: Allophone patients who received language-concordant care had lower risk of adverse events (adjusted OR 0.25, 95% confidence interval [CI] 0.15-0.43) and in-hospital death (adjusted OR 0.44, 95% CI 0.29-0.66), as well as shorter stays in hospital (adjusted rate of mean 0.74, 95% CI 0.66-0.83) than allophone patients who received language-discordant care. Results were similar for Francophone patients, although the magnitude of the effect was smaller than for allophone patients. Language concordance or discordance of the hospital admission was not associated with significant differences in postdischarge outcomes. INTERPRETATION: Patients who received most of their care from physicians who spoke the patients' primary language had better in-hospital outcomes, suggesting that disparities across linguistic groups could be mitigated by providing patients with language-concordant care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".