Stand Up for Indigenous Health: A Simulation to Educate Residents About the Social Determinants of Health Faced by Indigenous Peoples in Canada
Bibliographic record
Abstract
PROBLEM: In Canada, Indigenous peoples face significant health disparities. To improve health outcomes of and provide culturally safe care to Indigenous patients, medical learners must receive training on the social determinants of health (SDOH) driving these health inequities. The authors developed Stand Up for Indigenous Health (SU4IH), an immersive 2-hour simulation where participants navigate a series of scenarios as an Indigenous person. The objective of this pilot study was to assess whether SU4IH promotes intercultural empathy and enhances medical learners' knowledge of Indigenous SDOH. APPROACH: The authors partnered with 4 Indigenous communities in Ontario, Canada, from urban, rural, and remote settings to develop the scenarios for SU4IH between June 2015 and March 2016. During each SU4IH simulation, learners experience 14 scenarios using the Stand Up for Health mobile app, which automatically calculates each individual's financial balance and stress levels as the simulation unfolds. The authors conducted a pre-post intervention study of SU4IH in January 2019 with family medicine residents recruited from 2 training sites in Ontario (n = 29). Residents completed pre- and postsurveys assessing change in empathy toward Indigenous patients (primary outcome), knowledge of Indigenous SDOH (secondary outcome), and motivation to engage with Indigenous patients in a culturally safe manner (secondary outcome). OUTCOMES: Residents' empathy scores significantly increased after participating in SU4IH (P < .001), as did residents' knowledge of Indigenous SDOH (P < .001) and motivation to engage with Indigenous patients in a culturally safe manner (P = .031). NEXT STEPS: The authors are working to expand their capacity to implement this learning tool across Canada, which has involved relationship building with medical learners and faculty outside of Ontario who will need to partner with Indigenous communities in their region to develop region-specific scenarios. SU4IH is also being redesigned for small-group and virtual formats to facilitate its expansion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".