First Nations emergency care in Alberta: descriptive results of a retrospective cohort study
Bibliographic record
Abstract
Abstract Background Worse health outcomes are consistently reported for First Nations people in Canada. Social, political and economic inequities as well as inequities in health care are major contributing factors to these health disparities. Emergency care is an important health services resource for First Nations people. First Nations partners, academic researchers, and health authority staff are collaborating to examine emergency care visit characteristics for First Nations and non-First Nations people in the province of Alberta. Methods We conducted a population-based retrospective cohort study examining all Alberta emergency care visits from April 1, 2012 to March 31, 2017 by linking administrative data. Patient demographics and emergency care visit characteristics for status First Nations persons in Alberta, and non-First Nations persons, are reported. Frequencies and percentages (%) describe patients and visits by categorical variables (e.g., Canadian Triage and Acuity Scale). Means, medians, standard deviations and interquartile ranges describe continuous variables (e.g., age). Results The dataset contains 11,686,288 emergency care visits by 3,024,491 unique persons. First Nations people make up 4% of the provincial population and 9.4% of provincial emergency visits. The population rate of emergency visits is nearly 3 times higher for First Nations persons than non-First Nations persons. First Nations women utilize emergency care more than non-First Nations women (54.2% of First Nations visits are by women compared to 50.9% of non-First Nations visits). More First Nations visits end in leaving without completing treatment (6.7% v. 3.6%). Conclusions Further research is needed on the impact of First Nations identity on emergency care drivers and outcomes, and on emergency care for First Nations women.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".