Search results for "Injury epidemiology"

showing 2 items of 2 documents

MAVIE-Lab Sports: a mHealth for Injury Prevention and Risk Management in Sport

2018

International audience; Smart-phones technology and the development of mHealth (Mobile Health) applications offer an opportunity to design intervention tools to influence health behavior changes. The MAVIE-Lab is a mHealth application including a DSS (Desicion Support System) to assist in the personalized evaluation of HLIs (Home, Leisure and Sport Injuries) risk and to promote the adoption of prevention measures. MAVIE-Lab Sports will be the first module of the mobile application. The purpose of this PhD project is to improve a particular module of MAVIE-Lab, devoted to sports (MAVIE-Lab Sports), in different aspects: statistical modeling, design and ergonomics. It also aims to evaluate sy…

Process managementComputer scienceInjury030501 epidemiologyMathematics of computing[STAT.CO] Statistics [stat]/Computation [stat.CO][ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG]Bayesian networks BN03 medical and health sciences[STAT.ML]Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][STAT.AP] Statistics [stat]/Applications [stat.AP]Personal digital assistantsInjury preventioneHealthInjury Epidemiology[STAT.CO]Statistics [stat]/Computation [stat.CO]mHealthRisk managementComputingMilieux_MISCELLANEOUS[ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML][ STAT.CO ] Statistics [stat]/Computation [stat.CO][STAT.AP]Statistics [stat]/Applications [stat.AP]030505 public healthHome and leisure injuries[STAT.ME] Statistics [stat]/Methodology [stat.ME]business.industryHLIs[ STAT.AP ] Statistics [stat]/Applications [stat.AP]Human factors and ergonomicsUsability[ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]Human-centered computing[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]Intervention (law)Bayesian networks[ STAT.ME ] Statistics [stat]/Methodology [stat.ME][SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologieHuman-centered computing[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologieeHealth0305 other medical sciencebusinessAppPrediction[STAT.ME]Statistics [stat]/Methodology [stat.ME]
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Predicting needlestick and sharps injuries in nursing students: Development of the SNNIP scale

2020

© 2020 The Authors. Nursing Open published by John Wiley & Sons Ltd. Aim: To develop an instrument to investigate knowledge and predictive factors of needlestick and sharps injuries (NSIs) in nursing students during clinical placements. Design: Instrument development and cross-sectional study for psychometric testing. Methods: A self-administered instrument including demographic data, injury epidemiology and predictive factors of NSIs was developed between October 2018–January 2019. Content validity was assessed by a panel of experts. The instrument's factor structure and discriminant validity were explored using principal components analysis. The STROBE guidelines were followed. Results: E…

cross-sectionalknowledgePsychometricsFactor structurenursing studentSettore MED/44 - MEDICINA DEL LAVORONursingpreventionSurveys and QuestionnairesContent validityneedlestickMedicineHealth belief modelHumanssharps injuriePsychometric testingNeedlestick InjuriesGeneral NursingResearch Articlesnursing studentslcsh:RT1-120validationlcsh:Nursingbusiness.industryInjury epidemiologyquestionnairesharps injuriesDiscriminant validityExploratory factor analysisCross-Sectional StudiesScale (social sciences)Students Nursingcross‐sectionalbusinesscross-sectional; Health Belief Model; knowledge; needlestick; nursing students; prevention; questionnaire; sharps injuries; validationResearch ArticleHealth Belief Model
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