Leaving emergency departments without completing treatment among First Nations and non–First Nations patients in Alberta: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Our previous research showed that, in Alberta, Canada, a higher proportion of visits to emergency departments and urgent care centres by First Nations patients ended in the patient leaving without being seen or against medical advice, compared with visits by non-First Nations patients. We sought to analyze whether these differences persisted after controlling for patient demographic and visit characteristics, and to explore reasons for leaving care. METHODS: We conducted a mixed-methods study, including a population-based retrospective cohort study for the period of April 2012 to March 2017 using provincial administrative data. We used multivariable logistic regression models to control for demographics, visit characteristics, and facility types. We evaluated models for subgroups of visits with pre-selected illnesses. We also conducted qualitative, in-person sharing circles, a focus group, and 1-on-1 telephone interviews with health directors, emergency care providers, and First Nations patients from 2019 to 2022, during which we reviewed the quantitative results of the cohort study and asked participants to comment on them. We descriptively categorized qualitative data related to reasons that First Nations patients leave care. RESULTS: Our quantitative analysis included 11 686 287 emergency department visits, of which 1 099 424 (9.4%) were by First Nations patients. Visits by First Nations patients were more likely to end with them leaving without being seen or against medical advice than those by non-First Nations patients (odds ratio 1.96, 95% confidence interval 1.94-1.98). Factors such as diagnosis, visit acuity, geography, or patient demographics other than First Nations status did not explain this finding. First Nations status was associated with greater odds of leaving without being seen or against medical advice in 9 of 10 disease categories or specific diagnoses. In our qualitative analysis, 64 participants discussed First Nations patients' experiences of racism, stereotyping, communication issues, transportation barriers, long waits, and being made to wait longer than others as reasons for leaving. INTERPRETATION: Emergency department visits by First Nations patients were more likely to end with them leaving without being seen or against medical advice than those by non-First Nations patients. As leaving early may delay needed care or interfere with continuity of care, providers and departments should work with local First Nations to develop and adopt strategies to retain First Nations patients in care.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".