Factors counteracting nurses' bullying in healthcare organizations: An applied study in King Abdul-Aziz University Hospital in Jeddah Province
Bibliographic record
Abstract
In a time of diminishing human resources in the field of healthcare, bullying among nursing staff is a challenge that has a negative effect on the individual nurse, work team, patient care, and consequently, on the entire healthcare organization. This study aims to investigate the presence of policy that addresses bullying, nurses’ knowledge and skills in dealing with bullying behaviour, and the role of nursing managers in counteracting bullying among nurses in the workplace. A quantitative descriptive study utilizing self-administered questionnaires was undertaken. The key results to come out from this study show that the presence of workplace code of ethics policies to address bullying behaviours and the role of nursing managers do not contribute to counteracting bullying among nurses in the selected hospital. However, nurses indicated the significance of knowledge and skills in counteracting bullying. The study conclusions contribute a number of findings of importance to the field of nursing management. The study disclosed significant correlations between the healthcare organization's bullying behaviours policy, managers’ roles, and nurses’ knowledge and skills in counteracting bullying. Nurses’ workplace bullying behaviours can be managed and eradicated by means of the collaboration of the three parties: nurse managers, policy, and nurses’ knowledge and skills. This study has considerable implications for nursing management in counteracting bullying among nurses. Managers have a fundamental role in counteracting bullying, for example by setting policy and conducting organization-wide surveys to study nurses’ job satisfaction, therefore, recommendations include mandatory peer reviews as part of an annual appraisal for each nurse, the initiation of a formal system to report behavioural issues among healthcare staff and the establishment of a committee to respond to, and deal with, behavioural issues among the organization’s staff.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".