The Impact of Language on Emergency Department Visits, Hospitalizations, and Length of Stay Among Home Care Recipients
Bibliographic record
Abstract
BACKGROUND: Research considering the impact of language on health care utilization is limited. We conducted a population-based study to: (1) investigate the association between residents' preferred language and hospital-based health care utilization; and (2) determine whether this association is modified by dementia, a condition which can exacerbate communication barriers. METHODS: We used administrative databases to establish a retrospective cohort study of home care recipients (2015-2017) in Ontario, Canada, where the predominant language is English. Residents' preferred language (obtained from in-person home care assessments) was coded as English (Anglophones), French (Francophones), or other (Allophones). Diagnoses of dementia were ascertained with a previously validated algorithm. We identified all emergency department (ED) visits and hospitalizations within 1 year. RESULTS: Compared with Anglophones, Allophones had lower annual rates of ED visits (1.3 vs. 1.8; P<0.01) and hospitalizations (0.6 vs. 0.7; P<0.01), while Francophones had longer hospital stays (9.1 vs. 7.6 d per admission; P<0.01). After adjusting for potential confounders, Francophones and Allophones were less likely to visit the ED or be hospitalized than Anglophones. We found evidence of synergism between language and dementia; the average length of stay for Francophones with dementia was 25% (95% confidence interval: 1.10-1.39) longer when compared with Anglophones without dementia. CONCLUSIONS: Residents whose preferred language was not English were less frequent users of hospital-based health care services, a finding that is likely attributable to cultural factors. Francophones with dementia experienced the longest stays in hospital. This may be related to the geographic distribution of Francophones (predominantly in rural areas) or to suboptimal patient-provider communication.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".