Exploring Icebreakers in Nursing Education Through a Mixed-Methods Design: Helpful or Harmful?
Bibliographic record
Abstract
Background: Icebreakers share the common goal of promoting interaction. Despite the overwhelming positive regard for icebreakers in nursing education, they may have unintended consequences, such as highlighting inequities or perpetuating microaggressions. This research project incorporated the concept of microaggressions to explore how undergraduate nursing students experienced icebreakers within the classroom setting. Methods: This mixed-methods exploratory sequential design study used a quantitative survey followed by a semi-structured focus group using interpretive description thematic analysis. Results: A total of 43 students completed the quantitative survey, and three students participated in the follow-up focus group. The findings demonstrate that although well intentioned, icebreakers can also cause harm. Three themes were generated: revealing inequities, unveiling multiple tensions, and identifying conflict between purpose and outcome. Conclusion: Through our small study, we found that icebreakers can detract from content delivery, reveal inequities, and be divisive rather than contribute to a greater sense of belonging for students.
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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 | 0.040 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".