Hoffmann, R. A wiki for the life sciences where authorship matters. Nature Genetics (2008)

Editor

Personal info

View

About

Terms

Javascript disabled

Please enable Javascript support in your browser to use this application.

Instructions on how to enable JavaScript on different browsers can be found here: http://www.google.com/support/bin/answer.py?answer=23852.

[edit this page]

De Champlain, A. et al.

De Champlain, Sample, Dillon, Boulet,

Modeling longitudinal performances on the United States medical licensing examination and the impact of sociodemographic covariates: an application of survival data analysis.

BACKGROUND: This study models time to passing United States Medical Licensing Examination (USMLE) for the computer-based testing ( CBT) start-up cohort using the Cox Proportional Hazards Model. METHOD: The number of days it took to pass Step 3 was treated as the dependent variable in the model. Covariates were: (1) gender; (2) native language (English or other); (3) medical school location (United States or other); and (4) citizenship (United States or other). RESULTS: Examinees were .59 times as likely to pass USMLE if they were trained abroad. Additionally, examinees who reported having English as their primary language and U.S. citizenship were more likely to ultimately pass USMLE. Finally, though gender was also associated with passing USMLE, its practical significance was very small. CONCLUSION: Cox regression provides a useful tool for modeling performances in a continuous delivery model. Findings suggest that passing the USMLE sequence tends to be associated with native English-speaking USMGs who also hold U.S. citizenship.[1]

References

Modeling longitudinal performances on the United States medical licensing examination and the impact of sociodemographic covariates: an application of survival data analysis. De Champlain, A., Sample, L., Dillon, G.F., Boulet, J.R. Academic medicine : journal of the Association of American Medical Colleges. (2006) [Pubmed]

Annotations and hyperlinks in this abstract are from individual authors of WikiGenes or automatically generated by the WikiGenes Data Mining Engine. The abstract is from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.About WikiGenes Open Access Licence Privacy Policy Terms of Use apsburg

The world's first wiki where authorship really matters (Nature Genetics, 2008). Due credit and reputation for authors. Imagine a global collaborative knowledge base for original thoughts. Search thousands of articles and collaborate with scientists around the globe.