Bibliographic record
Abstract
Introduction Racism, discrimination and microaggressions experienced by underrepresented nursing students contribute to a loss of confidence, and feelings of sadness and anger.1,2,3 These experiences affect the students’ academic performance, and personal wellness. 1,2,3 There is a need for innovative and accessible resources that provide instruction and promote critical thinking regarding racism and microaggressions in the classroom and clinical setting. The aim of the CHARM project is to provide a toolkit for nursing students regarding how to respond safely and effectively to microaggressions. Method The use of eLearning and simulation games have successfully been used to provide education and problem-solving skills in healthcare when interacting with various groups of individuals. A group of seven nursing students are collaborating with faculty to develop an eLearning Toolkit and virtual simulation games (VSGs) focused on addressing racism and microaggressions. Results To date, the outline of a website that includes access to a pre-learning module and 5 VSGs have been designed. The pre-learning module includes definitions and education regarding the 6-step method to responding to microaggressions.4,5 The VSGs immerse the players into various scenarios where they are provided with options on how to respond in clinical situations and the potential consequences of the response they choose. Discussion This project is a work in progress, and it is hoped that this project will support nursing students, so that they feel less alone. In addition, we hope to improve their confidence and provide resources in managing encounters where racism and microaggressions occur.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".