The impact of hospital language on the rate of in-hospital harm. A retrospective cohort study of home care recipients in Ontario, Canada
Bibliographic record
Abstract
Abstract Background Patients who live in minority language situations are generally more likely to experience poor health outcomes, including harmful events. The delivery of healthcare services in a language-concordant environment has been shown to mitigate the risk of poor health outcomes related to chronic disease management in primary care. However, data assessing the impact of language-concordance on the risk of in-hospital harm are lacking. We conducted a population-based study to determine whether admission to a language-discordant hospital is a risk factor for in-hospital harm. Methods We used linked administrative health records to establish a retrospective cohort of home care recipients (from 2007 to 2015) who were admitted to a hospital in Eastern or North-Eastern Ontario, Canada. Patient language (obtained from home care assessments) was coded as English (Anglophone group), French (Francophone group), or other (Allophone group); hospital language (English or bilingual) was obtained using language designation status according to the French Language Services Act. We identified in-hospital harmful events using the Hospital Harm Indicator developed by the Canadian Institute for Health Information. Results The proportion of hospitalizations with at least 1 harmful event was greater for Allophones (7.63%) than for Anglophones (6.29%, p
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".