Observational study on resource utilisation of patients with limited English proficiency (LEP) at a high-LEP serving community hospital emergency department
Bibliographic record
Abstract
Communication barriers often result in healthcare disparities. Language barriers in patients with limited English proficiency (LEP) frequently results in higher healthcare expenditures and potentially poorer patient-centred outcomes. Therefore, we decided to assess resource utilisation of patients with LEP at our high-LEP serving community hospital emergency department (ED) in Canada. Specifically, we examined whether LEP patients have a higher rate of CT utilisation and/or a higher rate of hospital admission from the ED.We enrolled 100 patients who presented to the ED in our study. Each patient's English proficiency was rated. We classified 31 patients as LEP patients and 69 patients as non-LEP patients. Within the LEP patients' group, 13 out of 31 patients (42%) received a CT scan, while in the non-LEP patients' group, 30 out of 69 patients (43%) received a CT scan. In addition, 28 out of 31 patients (90%) from the LEP patients' group were admitted to the hospital after the initial ED consultation, while in the non-LEP patients' group, 51 out of 69 patients (74%) were admitted.We did not find a difference in CT scan utilisation between LEP and non-LEP patients (p=0.89). Although there is a trend towards a higher hospital admission rate in LEP patients, our finding was not statistically significant (p=0.062).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".