How to Construct Simulations Regarding Care Provisions for Indigenous Peoples
Bibliographic record
Abstract
[para. 1]: "Benefits of delivering simulation and the processes that developers undertook to create simulations on diverse topics have been extensively published. What has not been largely examined is how to develop simulations regarding care provisions to Indigenous peoples, to teach students about care delivery with consideration of cultural practices of diverse groups that have and continue to experience inequities within the Canadian health care system. Indigenous people is a collective name used to describe the descendants and the original peoples of North America. While it is impossible to learn intricacies of every culture, learning how to communicate with empathy, display cultural humility, and respond to clients' verbal and nonverbal cues can establish a strong collaborative relationship with clients and families. Simulation can expose learners to diverse clients and foster development of therapeutic communication with them, irrespective of their differences. Ensuring that nurses take culture into account when caring for clients remains a major goal for the discipline. This article details the simulation development process the team undertook and lessons learned from an actor simulation involving an Indigenous client."
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".