Qualitative evaluation of a role play bullying simulation
Bibliographic record
Abstract
Bullying against nurses is becoming a pervasive problem. In this article, a role play simulation designed for undergraduate nursing students is described. In addition, the evaluation findings from a subsample of students who participated in a role play simulation addressing bullying behaviors are reported. Focus group sessions were completed with a subset of eight students who participated in the intervention. Sessions were audiorecorded, transcribed verbatim, and analyzed using Colaizzi's procedural steps for qualitative analysis. Themes derived from the data were "The Experience of Being Bullied", "Implementation of the Program", "Desired Outcome of the Program", and "Context of Bullying in the Nursing Profession". Role play simulation was an effective and active learning strategy to diffuse education on bullying in nursing practice. Bullying in nursing was identified as a problem worthy of incorporation into the undergraduate nursing curriculum. To further enhance the learning experience with role play simulation, adequate briefing instructions, opportunity to opt out of the role play, and comprehensive debriefing are essential.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".