Bullying among medical and nonmedical students at a university in Eastern Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Many medical students, junior doctors, and other health-care professionals have been affected by the negative experience of bullying. Research is scarce on bullying experienced by medical and nonmedical students in Saudi Arabia unlike what is found in Western countries. It is unclear whether being a nonmedical student modifies the risk of being bullied. MATERIALS AND METHODS: A cross-sectional study included 400 university students using convenient sampling. The sample comprised 295 students who responded and were stratified into medical (n = 176) and nonmedical (n = 119) groups. Statistical Package for the Social Sciences (SPSS) version 22.0 was used to analyze our data. Normality was measured using the Kolmogorov–Smirnov test. Statistical significance was tested using chi-square test for categorical variables, and t-test for continuous variables. RESULTS: Almost half of the respondents were found to have experienced some bullying, victimization, or other harassment during their medical education. The most common forms of bullying were verbal abuse and undue pressure to produce work (43.8%; n = 77). Nonmedical students experienced more bullying than medical students and were more likely to be female, single, and younger in age. The number of medical students subjected to sexual harassment (1.7%; n = 3) was higher than nonmedical students (0.8%; n = 1). Physical violence was more towards nonmedical (4.2%; n = 5) than medical students (1.1%, n = 2). The rates of bullying continue to be associated with anxiety and depression. CONCLUSIONS: Our data suggest similar bullying rates in the developed world but higher than previously reported in a Saudi study. Bullying or harassment affects both medical and nonmedical students and is associated with high levels of anxiety and depression.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".