DOL: Final Rule on Overtime Regulations; Fair Pay and Safe Workplaces Executive Order; Fiduciary Rule; Miscellaneous • EEOC: EEO-1 Reports; Religious Bias • IRS: Wellness Rewards; Marijuana • NLRB • OSHA: Final Rule to Modernize Injury Data Collection

2017 ◽  
Vol 43 (4) ◽  
pp. 63-69
Author(s):  
Howard M. Kastrinsky
Keyword(s):  
Data Collection ◽  
Executive Order ◽  
Injury Data ◽  
Final Rule ◽  
Religious Bias ◽  
Fair Pay

scholarly journals A cross sectional survey of international horse-racing authorities on injury data collection and reporting practices for professional jockeys

2021 ◽  
pp. 103686
Author(s):  
Siobhán O'Connor ◽  
Peta L Hitchens ◽  
Charlotte Bolwell ◽  
Rachel Annan ◽  
Adrian McGoldrick ◽  
...  
Keyword(s):  
Data Collection ◽  
Horse Racing ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Reporting Practices ◽  
Injury Data

Injury Severity: Better Data through Direct Physician Entry of Anatomic Injuries?

1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S45-S48 ◽  
Author(s):  
Brad M. Cushing ◽  
Sandra D. Teitelbaum ◽  
William Burman ◽  
David Karges ◽  
William Bame
Keyword(s):  
Data Collection ◽  
Injury Severity ◽  
Injury Data ◽  
Severity Scores ◽  
The Times

The authors compared the injury diagnoses and Injury Severity Scores (ISSs) generated by three data-collection and -coding methods, and examined the times needed and costs associated with the methods. One method involved direct electronic entry of injury data by a physician in the admitting area. Codes, severity scores, and times and costs varied significantly with the different methods, thus suggesting a need for further study of the derivation of injury severity codes.

WHO launches guide to fatal injury data collection

2012 ◽  
Vol 18 (6) ◽  
pp. 421-421
Author(s):  
Joan Ozanne-Smith ◽  
Kidist K Bartolomeos ◽  
Margie M Peden
Keyword(s):  
Data Collection ◽  
Fatal Injury ◽  
Injury Data

scholarly journals Green Cross: Application for analyzing School injuries

2017 ◽  
Vol 9 (4) ◽  
pp. 322-329 ◽  
Author(s):  
Brita Somerkoski
Keyword(s):  
Data Collection ◽  
Children And Adolescents ◽  
Qualitative Data ◽  
Human Factor ◽  
School Context ◽  
Near Miss ◽  
Unintentional Injuries ◽  
Collection Method ◽  
Injury Data ◽  
Potential Risks

Unintentional injuries are a major cause of untimely deaths among children and adolescents. Violence and injuries in the schools have raised the need to collect the injury data routinely and to find ways to analyze the potential risks of the near-miss cases. The aim of this study is to explore the injury data collection method piloted with the Green Cross software and to describe the characteristics of the school injuries (n=88). The qualitative data consisted of user-interviews and data reports. As the main result of this study, the Green Cross software provides a decent way to monitor the injuries in the school context in such a way that the accidents, incidents, injuries and near-miss cases become more visible. A novel finding was that many school injuries were unpredictable, connected to human factor issues, persons acting against norms and regulations or using structures or products in a way they are not supposed to be used.

scholarly journals A Review of Sports Injury Data Collection Literature and Recommendations for Future Research

2010 ◽  
Author(s):  
Stephen Bailey ◽  
Ebonie Scase ◽  
Michael Heynen ◽  
Mary Magarey
Keyword(s):  
Data Collection ◽  
Injury Prevention ◽  
Injury Severity ◽  
Relevant Literature ◽  
Sports Injury ◽  
Injury Surveillance ◽  
Future Research ◽  
Injury Data ◽  
Australian Rules Football ◽  
Injury Definition

Purpose: There is significant cost associated with sports injury. Establishing injury profiles in contact sports like Australian Rules Football (ARF) will facilitate implementation of injury prevention strategies. The purpose of this literature review was to investigate sports injury data collection methodology, assess the strengths and limitations of previous research and identify gaps in the relevant literature. Recommendations for methodology of future sports injury data collection studies are made, particularly with reference to junior ARF. Method: A non-systematic literature search was undertaken in a narrative fashion to determine past and current injury surveillance in Australian Rules Football (ARF), Soccer, Rugby league and Union. The comprehensive search was performed using databases Cinahl, Sport Discus, Medline, Science Direct, Scopus and Informit Health Database: Ausport. For inclusion in this review, studies had to include the collection of baseline data, be peer-reviewed and have full text versions available in English. The effect of the methodology on research outcomes was evaluated, including: epidemiology, aetiology, common mechanisms and risk factors for injury are evaluated, as are the incidence, prevalence, severity and pathologies of injury in relevant sports. Results: While other alternatives are presented and evaluated, the following criteria appear the most reliable for use in future studies. A narrow games/time lost injury definition; a similar narrow injury severity definition; and a prospective model for sports injury data collection, making use of a standardised Player Movement Record (PMR). The AFL is the only major sport in which an injury surveillance system has been created that is robust, reliable and has captured all data from all clubs over a 12 year period. This system uses all criteria outlined. Conclusions/Recommendations: A significant knowledge gap remains with respect to the analytical epidemiology of injuries at the non-elite level of participation and a very small evidence base exists for injury prevention in Australian football, especially at non-elite and junior levels. By using critically developed methodology in the form of a prospective cohort study, an updated sports injury data collection model, adequate sample size and consistent injury definition, future research will guide future injury prevention interventions in junior ARF.

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