Increased workplace bullying against nurses during COVID-19: A health and safety issue
Bibliographic record
Abstract
Nurses are the backbone of healthcare organizations. However, as frontline workers, nurses are regularly exposed to perilous conditions and workplace harassment, with a few or no avenues to report or seek adequate support. This causes frustration and stress among nurses and can eventually lead to compromised patient care. This also contributes to workplace bullying, which results in a toxic and stressful work environment. This problem is a global health and safety issue due to its highly negative impact on both individuals and organizations. Recent studies indicate that the COVID 19 pandemic has significantly increased incidents of workplace bullying against nurses. Several contributing factors have been highlighted, when considering the underlying causes of workplace bullying against nurses, including power disparity, organizational attributes, and the image of nurses, as portrayed in the media. Because the pandemic has brought the challenge of creating a safe work environment for nurses to the fore, now more than ever, healthcare organizations need to take bold actions to protect nurses. Nursing management needs to implement bullying prevention interventions that provide nurses with a safe work environment. Using empirical and theoretical literature as its basis, this paper aims to discuss workplace bullying against nurses and consider how this problem has been impacted by the COVID 19 pandemic. This paper recommends the application of a Socio Ecological Model (SEM), which provides evidence-based interventions intended to reduce workplace bullying against nurses.
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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 | 0.007 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".