Teaching Culturally Safe Care in Simulated Cultural Communication Scenarios During the COVID-19 Pandemic: Virtual Visits with Indigenous Animators
Bibliographic record
Abstract
Clinical learning activities involving Indigenous patient actors that specifically address the development of culturally safe care skills among medical students are important in order to improve health care for Indigenous people. In 2020, the COVID-19 pandemic led to strict physical distancing regulations and regional lockdowns that made the in-person delivery of Simulated Cultural Communication Scenarios (SCCS) with Indigenous patient actors impossible due to the disproportionate risk that public health emergencies pose for Indigenous communities. As the pandemic continued in 2021, we co-created a Virtual Visit approach to SCCS for the education of culturally safe care to pre-clerkship medical students. We report on student and tutor evaluation of these virtual sessions and contextualize our findings with our previous results delivering In-Person SCCSs. We found that Virtual Visit SCCS were highly effective in providing authentic exposure to and feedback from Indigenous patients. However, students rated their learning outcomes with Virtual Visit lower than the In-person approach to SCCS. We recommend formal training on interacting with patients in virtual care scenarios prior to Virtual Visit SCCS. We also found that exposure to SCCS with Indigenous animators has the potential to conjure up a diverse spectrum of sometimes unresolved negative feelings related to colonialism among students and tutors including discomfort, embarrassment, and anxiety. Our findings underscore the importance of resolving these sentiments within the safe environment of a classroom. To prepare Indigenous as well as non-Indigenous students and tutors adequately, it is important to acknowledge and critically deconstruct the embodiment of colonialism and Indigenous-settler relations when teaching physicians, as well as future physicians, preparedness for culturally safe care of Indigenous peoples.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".