In-Hospital Patient Harm Across Linguistic Groups: A Retrospective Cohort Study of Home Care Recipients
Bibliographic record
Abstract
Objective Research examining the impact of language barriers on patient safety is limited. We conducted a population-based study to determine whether patients whose primary language is not English are more likely to experience harm when admitted to hospitals in Ontario, Canada. Methods We used linked administrative health records to establish a retrospective cohort of home care recipients (from 2010 to 2015) who were subsequently admitted to hospital. Patient language (obtained from home care assessments) was coded as English, French, or other. Harmful events were identified using the Hospital Harm Indicator developed by the Canadian Institute for Health Information. Results We included 190,724 patients (156,186 Anglophones, 5,110 Francophones, and 29,428 Allophones). There was no significant difference in the unadjusted risk of harm for Francophones compared with Anglophones (relative risk [RR], 0.94; 95% confidence interval [CI], 0.87–1.02). However, Allophones were more likely to experience harm when compared with Anglophones (RR, 1.14; 95% CI, 1.10–1.18). The risk of harm was even greater for Allophones with low English proficiency (RR, 1.18; 95% CI, 1.13–1.24). After adjusting for potential confounders, Anglophones and Allophones were equally likely to experience harm of any type, but Allophones more likely to experience harm from infections and procedures. Conclusions Patients whose primary language was not English or French were more likely to experience harm after admission to hospital, especially if they had low English proficiency. For these patients, the risk of harm from infections and procedures persisted in the adjusted analysis, but the overall risk of harm did not.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".