A Study of Incivility in the Iranian Nursing Training System Based on Educators and Students’ Experiences: A Quantitative Content Analysis
Bibliographic record
Abstract
BACKGROUND: It is absolutely essential to know the negative impacts incivility in students and educators may have on the creation of a suitable teaching-learning environment. Better education of to-be nurses would improve their service to patients and society in the future. There has been no research in Iran so far on this particular case. This study examines the experiences of uncivil or disrespectful behavior from the standpoint of educators and students. METHODOLOGY & METHODS: A quantitative content analysis was carried out to study manuscripts presented in the form of open questionnaires. To this end, data produced from detailed answers from 640 students and educators were inputted into the computer and line-by-line and sentence-by-sentence coding was done. After that, implied codes were added, the categories were revealed, and finally counting frequency of code in categories was carried out. RESULTS: The most important categories that students considered uncivil behavior were waste of class time, distraction, incompetence in managing the class, discrimination, bad assessment, insult and threat on behalf of the educators. In contrast to their view, what the educators thought of as disrespectful included class disorder, humiliation of other students, irregular attendance of classes, bad sitting postures, non-observance of Islamic standards, and coming unprepared to the class by students. CONCLUSION: From the viewpoint of students and educators, incivility is present towards one another in the academic environment. This study determines the most important forms of the same from their stand point. Since disrespectful and threatening behavior has a significant impact on learning environment, we highly recommend a thorough examination to be carried out in future studies on the origin and the managing strategies of such behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".