Verbal abuse facing Saudi nurses during internship program
Bibliographic record
Abstract
Background: Verbal abuse, in the context of workplace violence, is increasing, with nurses at high liability of being subjected to it since they are the first line of care delivery. This phenomenon is now receiving greater recognition due to its negative impact on nurses. This study aims to assess the prevalence of verbal abuse faced by Saudi nursing intern students in Jeddah, Saudi Arabia.Methods: A cross-sectional study was conducted via a modified online questionnaire completed by Saudi intern nurses in Jeddah in March 2020. Descriptive statistical analysis was executed using statistical software SPSS Version 21.Results: From a total of 132 nurses, 94 participants met the study criteria. The findings show that 39.4% of Saudi intern nurses have experienced verbal abuse. The common perpetrators are patient/client (73%) and other staff members (73%), followed by patients’ relatives (45.9%). 48.6% of the participants did not report incidences of verbal abuse, commonly citing fear of the negative consequences (51.4%) and not knowing who to report it to (45.9%). A significant relation between working night shifts (6 pm to 7 am) and being verbally abused was found.Conclusions: Saudi nursing interns are vulnerable to verbal abuse. This study’s results highlight the possible risk to nursing interns, which may be diminished by modifying perceptions of verbal abuse and by clarifying the rules and regulations for both nursing interns and suspected perpetrators. We recommend future studies of verbal abuse are conducted in larger groups of nurses across different provinces in Saudi Arabia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".