Appropriately designed, evidence-based online CME can produce objectively measured changes in behavior as well as sustained gains in knowledge that are comparable or superior to those realized from effective live activities.
The Premedical Honors College (PHC) is an eight-year, BS-MD program created in 1994 by Baylor College of Medicine (BCM) and The University of Texas-Pan American (UT-PA) to increase the number of physicians addressing the health care needs of underserved populations in Texas. The PHC targets South Texas, a 13-county, medically underserved area with a population that is 82% Hispanic. To date, the PHC has had 159 matriculants and 71 graduates, of whom 60 (84.5%) have matriculated into medical school. These results are significant considering that in 1996, only four students from all five South Texas colleges (combined enrollment of 30000 students) were accepted to medical school. An outcomes study comparing PHC matriculants with students of similar academic ability, ethnicity, and interest in medicine revealed that the odds of medical school matriculation were seven times higher for PHC students than for non-PHC students. The PHC's initial success has been acknowledged by the Texas legislature, which recently passed a bill to promote the PHC's replication. In addition, the number of PHC students-of whom 95% are Mexican American-who matriculate into medical school annually is significant nationally. In 2001, only 386 U.S. medical school matriculants (2.3% of all matriculants) were Mexican American; 17 of these students (4.4%) were PHC graduates. If current trends continue, the PHC could significantly expand the number of physicians serving minority and medically underserved populations in Texas and the nation. Also, the PHC provides an opportunity for research on programs designed to create pathways from high school to medical school.
There are similarities and differences in the perceived transition needs of a diverse group of adolescents and adults with CHD. Both cohorts desire additional information during the transition process, including interest in use of a transition checklist to learn about managing their CHD. Adolescents are interested in interactions with other teens and adult mentors with CHD and are open to Internet-based interventions. Web-based initiatives could deliver CHD information and provide a platform for social media mentorship.
Although the algorithm for generating a best-subsets variable-selection routine in logistic regression was presented more than a decade ago, a search of the literature reveals that the technique is seldom applied. A possible explanation is its omission from popular statistical computing packages. Newer versions of the Statistical Analysis System (SAS) provide limited capabilities, and the Statistical Package for the Social Sciences (SPSS) does not allow for its calculation. Using a sample data set, this article briefly describes in nonmathematical terms how to implement and interpret results from a best-subsets logistic regression using SAS.The purpose of this article is to briefly describe a best-subsets approach to selecting optimal predictor variables in logistic regression. After laying the theoretical framework, the first section of the article suggests possible reasons for the absence of published empirical studies employing best-subsets logistic regression. Next, the procedural steps are described along with computer syntax. Finally, a sample data set is used to demonstrate application and interpretation of the variable-selection technique.Correspondence concerning this article should be sent to Jason E. King,
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