First Nations status and emergency department triage scores in Alberta: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Previous studies have found that race is associated with emergency department triage scores, raising concerns about potential health care inequity. As part of a project on quality of care for First Nations people in Alberta, we sought to understand the relation between First Nations status and triage scores. METHODS: We conducted a population-based retrospective cohort study of health administrative data from April 2012 to March 2017 to evaluate acuity of triage scores, categorized as a binary outcome of higher or lower acuity score. We developed multivariable multilevel logistic mixed-effects regression models using the levels of emergency department visit, patient (for patients with multiple visits) and facility. We further evaluated the triage of visits related to 5 disease categories and 5 specific diagnoses to better compare triage outcomes of First Nations and non-First Nations patients. RESULTS: First Nations status was associated with lower odds of receiving higher acuity triage scores (odds ratio [OR] 0.93, 95% confidence interval [CI] 0.92-0.94) compared with non-First Nations patients in adjusted models. First Nations patients had lower odds of acute triage for all 5 disease categories and for 3 of 5 diagnoses, including long bone fractures (OR 0.82, 95% CI 0.76-0.88), acute upper respiratory infection (OR 0.90, 95% CI 0.84-0.98) and anxiety disorder (OR 0.67, 95% CI 0.60-0.74). INTERPRETATION: First Nations status was associated with lower odds of higher acuity triage scores across a number of conditions and diagnoses. This may reflect systemic racism, stereotyping and potentially other factors that affected triage assessments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".