“Nurses eat their young”: A novel bullying educational program for student nurses
Bibliographic record
Abstract
Bullying is a known and ongoing problem against nurses. Interventions are needed to prepare nursing students to prevent and mitigate the bullying they will experience in their nursing practice. The purpose of this article is to describe the development process and utility of one such intervention for use by nursing faculty with nursing students prior to their students' entry into the profession. The educational program was critiqued by an advisory board and deemed to be relevant, clear, simple, and non-ambiguous indicating the program to have adequate content validity. The program then was pilot tested on five university campuses. Faculty members who implemented the educational program discussed (1) the program having value to faculty members and students, (2) challenges to continued program adoption, and (3) recommendations for program delivery. The proposed multicomponent, multiyear bullying educational program has the potential to positively influence nursing education and ultimately nursing practice. Findings from the pilot implementation of the program indicate the need to incorporate the program into additional nursing courses beginning during the sophomore year of the nursing curricula.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".