Management of Non-Physical Violence against Registered Nurses in Hospital Acute Care Setting
Bibliographic record
Abstract
BACKGROUND: The issue of non-physical violence against registered nurses in acute care hospital settings is a pressing concern. As per 2021 CDC figures, there is a prevalence of 38.8 incidents of non-physical violence per 100 nurses annually. Such incidents can lead to serious consequences, including behavioral changes and decreased job effectiveness. Addressing this challenge is essential for ensuring a safe and efficient working environment for healthcare professionals. AIM: The study aims to explore management practices related to non-physical violence against registered nurses (RNs) in hospital acute care (AC) settings. It aims to answer the following main question: What are the effective management practices that can identify the reasons for, and evaluate measures to prevent, non-physical violence against registered nurses in hospital acute care settings? METHODOLOGY: The study employed a systematic examination of electronic databases, utilizing sources from ProQuest, PsycINFO, and the Medline library for the period between 2016 and 2022. Six paramount studies that scrutinized non-physical violence against healthcare professionals were included, with relevant articles meticulously analyzed to yield valid conclusions. Thematic analysis was employed to decipher the patterns emerging from the selected studies. RESULTS: Notable themes encompassed the causative factors behind non-physical violence against registered nurses in hospitals and the strategies employed to alleviate such incidents. These factors include high work pressure and stress, inadequate training and professionalism, absence of person-centered care, and unawareness and issues with patient family members. CONCLUSION: This study affirms that targeted training programs could significantly augment nurses’ capabilities to handle non-physical violence effectively. A heightened level of synergy between healthcare personnel and hospital management is crucial to enable immediate reporting of incidents and pave the way for safer working conditions.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".