Emergency department visits among First Nations female adults in Alberta: a population-based study
Bibliographic record
Abstract
OBJECTIVE: Emergency departments (EDs) serve as a critical first access point for receiving healthcare for many First Nations members. However, ED experiences differ between First Nations and non-First Nations patients. Our objective is to quantify differences in ED visit characteristics for First Nations female and non-First Nations female adult patients in Alberta. METHODS: We used healthcare administrative data from April 1, 2012 until March 31, 2017 linked to First Nations identifying data. We included all female patients aged 18-54 with ED encounters in Alberta, Canada. We extracted patient characteristics (e.g., age, gender, First Nations status) and ED visit characteristics (e.g., day of week, acuity, diagnosis, disposition). Descriptive statistics were calculated for each of the population groups (i.e., First Nations female and non-First Nations female patients). Mixed effects modeling statistical analyses were conducted to account for clustering when assessing for significant differences between population groups. RESULTS: First Nations female patients who had ≥ 1 ED visit used the ED more compared to non-First Nations female patients (median 5, [IQR 2, 10]) vs. (median 2, [IQR 1, 4]), and a higher proportion of First Nations female visits resulted in admission (6.0% vs. 4.9%, p < 0.0001). First Nations female patients had a higher proportion of their visits diagnosed as related to unspecific findings, infection, cancer, obstetrical conditions, substance misuse/addictions, and mental health. CONCLUSION: Findings suggest a lack of access to culturally safe primary and specialty care that would allow treatment in community and support discharge from acute care. ED providers must understand the conditions that underly First Nations women's visits to take a culturally safe approach that does not blame patients for the number or type of ED visits they require.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".