Bullying as a stressor in the workplace has been evaluated in numerous settings. It has never been evaluated in the emergency medical service (EMS) environment where bullying can occur from many different sources. The Negative Acts Questionnaire-Revised (NAQ-R) is a 22-question validated tool for evaluating bullying. Our hypothesis was that we could identify a shortened version of the NAQ-R that identifies bullying as accurately as the full screening tool. This was a cross sectional study of EMS providers in our local EMS transport agency. The local EMS agency transports approximately 50,000 patients per year and is a paramedic level response system. Results on the NAQ-R were on a 5-point Likert scale for each of 22 different categories of bullying that were summed by adding each questions 1-5 response for the 22 questions. Respondents were also categorized as victims or non-victims of bullying based on being positive for any of the 22 types of bullying at least once a week. We performed a binomial decision tree analysis and a cross-validation. Data were collected from 153 providers. Mean age was 33 ± 10 years and 50% were male. Total years in EMS were 8 ± 8 years. NAQ-R summed results in our group ranged between 22 and 88, with an average of 40 ± 15. A NAQ-R score of 33 or less was 91% accurate in identifying non-victims and a score of 45 or more was 94% accurate in identifying victims. The majority at 51% (77/152) of respondents were victims of one or more types of bullying. A combination of five questions was 94% accurate in identifying a victim of bullying among EMS providers. Cross validation resulted in a misclassification risk estimate of 0.12 ± .03. NAQ-R bullying scores in EMS are similar or higher than numbers in other fields. Five questions on the NAQ-R were 94% accurate in identifying victims of bullying in EMS providers.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/10903127.2018.1470208 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Knowledge and behavior towards COVID-19 in the northern region of the state of Rio de Janeiro - Brazil.
J Infect Dev Ctries
December 2024
Faculdade de Medicina de Campos, Campos dos Goytacazes, Brazil.
Introduction: Despite efforts by health organizations to share evidence-based information, fake news hindered the promotion of social distancing and vaccination during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed COVID-19 knowledge and practices in a vulnerable area in northern Rio de Janeiro, acknowledging the influence of the complex social and economic landscape on public health perceptions.
Methodology: This cross-sectional study was conducted in Novo Eldorado - a low-income, conflict-affected neighborhood in Campos dos Goytacazes - using a structured questionnaire, following the peak of COVID-19 deaths in Brazil (July-December 2021).
HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.
BMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFA machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness.
Sci Rep
January 2025
College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua, 321004, China.
Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them.
View Article and Find Full Text PDFA cross-tissue transcriptome-wide association study identifies new susceptibility genes for benign prostatic hyperplasia.
Sci Rep
January 2025
Department of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, People's Republic of China.
Benign prostatic hyperplasia (BPH) is a prevalent urinary system disorder. Despite evidence of a significant genetic component from previous studies, the specific pathogenic genes and biological mechanisms are still largely unknown. The study utilized the FinnGen R10 dataset, encompassing 177,901 individuals (36,601 cases and 141,300 controls), and the GTEx v8 EQTLs files to conduct single-tissue and cross-tissue transcriptome-wide association studies (TWAS).
View Article and Find Full Text PDFInvestigation into the PSD characteristics of internally unstable soils susceptible to suffosion.
Sci Rep
January 2025
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, China.
Internal instability of embankment soils under seepage can occur in two distinct ways: suffusion and suffosion. Suffusion involves the removal of fine particles from the matrix without causing significant disturbance to the soil skeleton, while suffosion is characterized by the movement of fine particles accompanied by skeleton collapse or deformation. In terms of dam safety, suffosion poses a greater threat than suffusion.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!