Interventions to improve equity in emergency departments for Indigenous people: A scoping review
Bibliographic record
Abstract
BACKGROUND: Disparities in health outcomes, including increased chronic disease prevalence and decreased life expectancy for Indigenous people, have been shown across settings affected by white settler colonialism including Canada, the United States, Australia, and New Zealand. Emergency departments (EDs) represent a unique setting in which urgent patient need and provider strain interact to amplify inequities within society. The aim of this scoping review was to map the ED-based interventions aimed at improving equity in care for Indigenous patients in EDs. METHODS: This scoping review was conducted using the procedures outlined by Arksey and O'Malley and guidance on conducting scoping reviews from the Joanna Briggs Institute. A systematic search of MEDLINE, CINAHL, SCOPUS, and EMBASE was conducted. RESULTS: A total of 3636 articles were screened by title and abstract, of which 32 were screened in full-text review and nine articles describing seven interventions were included in this review. Three intervention approaches were identified: the introduction of novel clinical roles, implementation of chronic disease screening programs in EDs, and systems/organizational-level interventions. CONCLUSIONS: Relatively few interventions for improving equity in care were identified. We found that a minority of interventions are aimed at creating organizational-level change and suggest that future interventions could benefit from targeting system-level changes as opposed to or in addition to incorporating new roles in EDs.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".