Nurses’ Perception Toward Workplace Violence at Dammam Medical Tower, Saudi Arabia
Bibliographic record
Abstract
Context: Male and female nurses face violence in their workplace because of daily exposure to challenging situations as a result of dealing with different types of patients, visitors, and their families. Aim: The study aimed to assess nurses' perceptions toward workplace violence at Dammam Medical Tower, Saudi Arabia. Methods: A quantitative descriptive cross-sectional design was used to conduct this study. The sample size consisted of 300 nurses working at Dammam Medical Tower using a convenient sampling technique from January to March 2019 and using a modified tool obtained from ‘Survey on Workplace Violence’ by Massachusetts Nurse’s Association. Results: The most common workplace violence for the last two years was verbal abuse and threatening. Additionally, sexual assault was less violent in the workplace. Around one-third of nurses reported all incidents to management, and less than half of them stated that the management was supportive and tried to find a solution. However, only 10% of them underwent related training regarding workplace violence prevention. Also, more than a quarter of nurses reported that a clear policy and procedures addressing violence are needed to combat violence in the workplace. There is a significant difference between nurses who work in outpatients or emergency department and total violence incidents. Conclusion: Verbal abuse and threatening are deemed to be the most common violence being occurred in the workplace, while patients and relatives are the commonest offenders. The administration of the workplace should develop a clear policy to address the violent act in work and enhance the violence concept in the orientation courses.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".