Using simulation-based education to enhance anti-racism learning in nursing
Bibliographic record
Abstract
OBJECTIVE: To determine whether simulation-based education promotes anti-racism learning among nursing students. METHODS: This descriptive qualitative study explored how simulation-based education could support anti-racism education in undergraduate nursing curricula. The study consisted of three parts: (a) journal entries, (b) anti-racism workshops and (c) simulation-based education. The three anti-racism workshops were part of the simulation pre-work to prepare students to actively participate in four simulated participant anti-racism scenarios. Content analysis of journal entries using the Sensitizing, Taking Action, and Reflection (STAR) framework suggests that the anti-racism workshops raised awareness of self and others, as well as racism and anti-racism strategies among the participants. Fourteen participants were recruited. Ten provided consent and participated in at least one component of the study, six participated in the simulations, and five completed all 8 journal entries. RESULTS: Our findings indicated the participants engaged in a continuous cycle of sensitization and reflection, which broadened their awareness in four categories: self, others, racism, and anti-racism strategies. As a result, the anti-racism workshops increased awareness of racism among participants. In addition, both were willing and able to address racism and advocate for political change during the simulations. The student participants found the simulated scenarios gave them a greater sense of authenticity when confronting racism. CONCLUSIONS: Anti-racism workshops and SBE are effective ways to support anti-racism learning in undergraduate nursing students. We recommend academic institutions explore ways to integrate antiracism SBE into curricula to support antiracism praxis.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".