P033: Engaging Indigenous patients in addressing cultural safety in an emergency department: a pilot initiative
Bibliographic record
Abstract
Introduction: Cultural safety is integral to good clinical care, particularly for Indigenous patients. However, it remains poorly defined in emergency department care (ED). Practitioners at an urban Canadian ED serving a significant Indigenous population sought to engage with the community to define areas for improvement in culturally safe emergency department care. Methods: A participatory action approach was used. A Steering Committee was created, including emergency clinicians and Indigenous health researchers. The Committee collaborated with a local Indigenous health study (Our Health Counts) to aid recruitment. Relevant Indigenous community organizations were identified and engaged via email and personal visits. Recruitment posters were placed in common areas at community sites and the ED. Convenience and snowball sampling was used - potential participants called an ED research coordinator and inclusion criteria were confirmed (self identify as Indigenous, >18 years old, ED visit within the past year). Eligible participants were invited to attend a focus group facilitated by an Aboriginal Elder. Results: 31 individuals called to enroll for a total of 4 potential focus groups. 1 was successfully held: 5 participants were confirmed, 2 attended. Many recruitment challenges were identified, including difficulty maintaining contact/follow-up with a transient population, poster dissemination before recruitment start date, non-Indigenous patients attracted by compensation, and potential participant safety concerns regarding non-Indigenous contact point. Conclusion: Our initiative highlights challenges in engaging vulnerable populations in a large city. Focus groups may be logistically too challenging for this transient population. Other real-time data collection methods, such as phone interviews or surveys may be promising. An Indigenous contact point would likely improve perceived safety. The lack of socio-demographic data collection makes identifying potential participants challenging.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".