Characteristics of First Nations patients who take their own leave from an inner‐city emergency department, 2016–2020
Bibliographic record
Abstract
OBJECTIVE: Using a strength-based framework, we aimed to describe and compare First Nations patients who completed care in an ED to those who took their own leave. METHODS: Routinely collected adult patient data from a metropolitan ED collected over a 5-year period were analysed. RESULTS: A total of 6446 presentations of First Nations patients occurred from 2016 to 2020, constituting 3% of ED presentations. Of these, 5589 (87%) patients waited to be seen and 857 (13%) took their own leave. Among patients who took their own leave, 624 (73%) left not seen and 233 (27%) left at own risk after starting treatment. Patients who were assigned a triage category of 4-5 were significantly more likely to take their own leave (adjusted odds ratio [OR] 3.17, 95% confidence interval [CI] 2.67-3.77, P < 0.001). Patients were significantly less likely to take their own leave if they were >60 years (adjusted OR 0.69, 95% CI 1.01-1.36, P = 0.014) and had private health insurance (adjusted OR 0.61, 95% CI 0.45-0.84, P < 0.001). Patients were more likely to leave if they were women (adjusted OR 1.17, 95% CI 1.01-1.36, P = 0.04), had an unknown housing status (adjusted OR 1.76, 95% CI 1.44-2.15, P < 0.001), were homeless (adjusted OR 1.50, 95% CI 1.22-1.93, P < 0.001) or had a safety alert (adjusted OR 1.60, 95% CI 1.35-1.90, P < 0.001). CONCLUSION: A lower triage category is a strong predictor of First Nations patients taking their own leave. It has been documented that First Nations patients are under-triaged. One proposed intervention in the metropolitan setting is to introduce practices which expediate the care of First Nations patients. Further qualitative studies with First Nations patients should be undertaken to determine successful approaches to create equitable access to emergency healthcare for this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 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".