Turkish nursing students’ perceptions and experiences of bullying behavior in nursing education
Bibliographic record
Abstract
Objective: This descriptive study aimed to determine the bullying and harassment experiences of nursing students’ in various Nursing Schools in Turkey. The types and frequency of bullying behaviors, the sources of bullying behaviors, and students’ emotions towards these experiences were investigated. Methods: Study participants were 370 undergraduate nursing students from four different Turkish Nursing Schools. To estimate bullying at nursing school I used a short version of the Negative Acts Questionnaire that adapted according to the earlier studies on bullying against nursing students particularly those conducted by Cooper et al. and Celik and Bayraktar. Results: A total of 222 respondents (60%) reported that they had experienced at least one of the thirteen bullying behaviors at daily and weekly frequencies during the last six months. Work related bullying was the most frequently encountered type of bullying behaviors which is followed by personal related bullying behaviors. Also, an interesting result from this study is that most students reported clinic nurses as their bully, indicating that the perpetrators were mostly females and older than them. Conclusions: This study supports previous reports of bullying against Turkish student nurses and adds to the scant body of literature showing that nursing students often experience bullying and harassment from clinical nurses (horizontal bullying), and importantly, this may influence their future employment choices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".