Improving Indigenous health equity within the emergency department: a global review of interventions
Bibliographic record
Abstract
INTRODUCTION: Indigenous health equity interventions situated within emergency care settings remain underexplored, despite their potential to influence patient care satisfaction and empowerment. This study aimed to systematically review and identify Indigenous equity interventions and their outcomes within acute care settings, which can potentially be utilized to improve equity within Canadian healthcare for Indigenous patients. METHODS: A database search was completed of Medline, PubMed, Embase, Google Scholar, Scopus and CINAHL from inception to April 2023. For inclusion in the review, articles were interventional and encompassed program descriptions, evaluations, or theoretical frameworks within acute care settings for Indigenous patients. We evaluated the methodological quality using both the Joanna Briggs Institute checklist and the Ways Tried and True framework. RESULTS: Our literature search generated 122 publications. 11 articles were selected for full-text review, with five included in the final analysis. Two focusing on Canadian First Nations populations and three on Aboriginal Australians. The main intervention strategies included cultural safety training, integration of Indigenous knowledge into care models, optimizing waiting-room environments, and emphasizing sustainable evaluation methodologies. The quality of the interventions was varied, with the most promising studies including Indigenous perspectives and partnerships with local Indigenous organizations. CONCLUSIONS: Acute care settings, serving as the primary point of access to health care for many Indigenous populations, are well-positioned to implement health equity interventions such as cultural safety training, Indigenous knowledge integration, and optimization of waiting room environments, combined with sustainable evaluation methods. Participatory discussions with Indigenous communities are needed to advance this area of research and determine which interventions are relevant and appropriate for their local context.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".