COMPARING INTENSIVE CARE UNIT AND EMERGENCY DEPARTMENT NURSES AT RISK FOR WORKPLACE VIOLENCE ON THEIR PERCEPTION OF SAFETY
Bibliographic record
Abstract
The concept of Workplace Violence (WPV) is relatively old, and spans across a multitude of disciplines. With the recent study on Emergency Department Violence by Gacki-Smith et al. (2010), new information has highlighted its effect on nursing. While studies regarding the impact of WPV in nursing are limited to date, there is evidence that employing an awareness of types of violence in nursing to the intensive care unit settings provides a number of safety advantages. These advantages include increased employee satisfaction, and increased patient safety. According to Kansagra (2008), ???the efficacy of violence prevention education in reducing the actual number of events is an area that clearly deserves further study??? (p. 1273). \nThis study was designed to compare two hospital units that are at risk, the intensive care unit (ICU) and the emergency department (ED) for violence with the unit employees??? perception of safety. The Occupational Health and Safety Council of Ontario's (OSHCO) WPV Survey will be utilized to determine the unit employees??? perception of safety. Workers who did not express a perception of safety at their unit are hypothesized to have experienced a higher incidence of violence. WPV can have a profound effect on the employee and should be regarded as a potential patient safety hazard.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".