Language and Length of Stay in the Pediatric Emergency Department
Bibliographic record
Abstract
BACKGROUND: Quality and accessibility of care for patients presenting to the emergency department (ED) can be limited if they cannot communicate in the same language as their health care provider. STUDY OBJECTIVES: We aimed to determine if children whose parents speak a primary language other than English have a longer length of stay (LOS) in the ED compared with English-speaking families. METHODS: We reviewed computerized ED records of age-matched English and 4 most common non-English languages in a tertiary pediatric hospital in Toronto, Canada. We randomly chose English-speaking families in a 3:1 ratio with non-English. We performed bivariate analyses and a multivariable linear regression to test the relationship between language, triage score, age, gender, day of the week, and diagnostic grouping. RESULTS: Out of 48,497 visits for 1 year, we included 6051 English-, 628 Spanish-, 486 Cantonese-, 486 Mandarin-, and 417 Tamil-speaking families. The average LOS was 3.86 and 3.95 hours for English and non-English-speaking patients, respectively (P > 0.05). Non-English speakers had lower acuity more frequently (P = 0.004) and arrived more over weekdays (P = 0.02). In the multivariate regression model, language, triage score, age, and gender were all significantly associated with LOS. Only 6% of the variance in LOS was explained by the regression model. CONCLUSIONS: Language, triage score, patient age, and gender are significantly associated with LOS in the ED. Among other interventions, securing ways to accommodate non-English-speaking health providers in the ED can possibly shorten the LOS and reduce nonacute visits to the ED.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".