ArticleAnnals of general psychiatry2017
A new technique to measure online bullying: online computerized adaptive testing.
Article in Annals of general psychiatry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 25 citations in OpenAlex.
- An app for predicting nurse intention to quit the job using artificial neural networks (ANNs) in Microsoft Excel.Medicine · 2022Article
- Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study.JMIR medical informatics · 2022Article
- Workplace gender-based violence and associated factors among university women in Enugu, South-East Nigeria: an institutional-based cross-sectional study.BMC women's health · 2021Article
- An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study.JMIR mHealth and uHealth · 2020Article
- An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study.JMIR medical informatics · 2020Article
- An App for Identifying Children at Risk for Developmental Problems Using Multidimensional Computerized Adaptive Testing: Development and Usability Study.JMIR pediatrics and parenting · 2020Article
- Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study.Journal of medical Internet research · 2020Article
- Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan.BMC health services research · 2019Article
- Application of a multidimensional computerized adaptive test for a Clinical Dementia Rating Scale through computer-aided techniques.Annals of general psychiatry · 2019Article
- Assessment of workplace bullying: reliability and validity of an Arabic version of the Negative Acts Questionnaire-Revised (NAQ-R).BMJ open · 2018Article
- Hospital nurses' attitudes, negative perceptions, and negative acts regarding workplace bullying.Annals of general psychiatry · 2017Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWorkplace bullying has been measured in many studies to investigate mental health issues. None uses online computerized adaptive testing (CAT) with cutting points to report bully prevalence at workplace.
objectiveTo develop an online CAT to examine person being bullied and verify whether item response theory-based CAT can be applied online for nurses to measure exposure to workplace bullying.
methodsA total of 963 nurses were recruited and responded to the 22-item Negative Acts Questionnaire-Revised (NAQ-R). All non-adaptive testing (NAT) items were calibrated with the Rasch rating scale model. Three scenarios (i.e., NAT, CAT, and the randomly selected method to NAT) were manipulated to compare their response efficiency and precision by comparing (i) item length for answering questions, person measure, (ii) correlation coefficients, (iii) paired
resultsThe NAQ-R is a unidimensional construct that can be applied for nurses to measure exposure to workplace bullying on CAT. CAT required fewer items (=8.9) than NAT (=22, an efficient gain of 60% =1-8.9/22). Nursing measures derived from both tests (CAT and the random to NAT) were highly correlated (
conclusionThe CAT-based NAQ-R reduces respondents' burden without compromising measurement precision and increases endorsement efficiency. The online CAT is recommended for assessing nurses using the criteria at -0.7 and 0.7 (or <30 and <60 in summed score) to identify bully grade as one of the three levels (high, moderate, and low). The bullied nurse can get help from a psychiatrist or a mental health expert at an earlier stage.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.