Addressing Culturally Based Hidden Bias and RacisM (A-CHARM) Using Simulation Experiences, Nik’s Story: A Quasi-Experimental Study
Bibliographic record
Abstract
Background/Purpose Racism and hidden bias experienced by underrepresented nursing students contribute to a loss of confidence and anxiety. The A-CHARM nursing project developed virtual simulation experiences for nursing students to practice how to address racism. ‘Nik's Story’ virtual simulation was created as part of the A-CHARM project. The purpose of this study was to examine the effectiveness of an education intervention, that included Nik's story, on cultural humility and cultural diversity awareness. Method This quasi-experimental study included a convenience sample of final year nursing students. After informed consent, participants completed a pre-intervention questionnaire that included the Cultural Humility Scale “context for difference in perspective” subscale, and the Cultural Diversity Awareness questionnaire to assess baseline knowledge. Students participated in an education intervention that included a lecture, Nik's story virtual simulation experience, a debrief and then completed a post-education/simulation questionnaire that included usability/learner engagement questions and the Cultural Humility Scale “context for difference in perspective” subscale, and the Cultural Diversity Awareness questionnaire. Results Forty-seven students consented and completed the pre/post intervention questionnaire. Participants rated the effectiveness, engagement and usability of the simulation experience highly. There was a significant positive change in cultural humility “context for difference in perspective” subscale (pre-scores = 6.9, SD = 3.3; post-scores = 31.0, SD = 3.8, p < 0.001), and cultural diversity awareness (pre-scores = 95.4, SD = 8.9; post-scores = 103.4, SD = 9.8, p < 0.001). Discussion This intervention was effective in improving cultural humility and cultural diversity awareness in nursing students. Conclusion Simulation experiences regarding racism in the clinical setting provide a strategy for students to learn how to professionally navigate unwanted experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".