Identifying the factors associated with medical students who fail Step 1 of the United States Medical Licensing Examination (USMLE) has been a focus of investigation for many years. Some researchers believe lower scores on the Medical Colleges Admissions Test (MCAT) are the sole factor used to identify failure. Other researchers believe lower course outcomes during the first two years of medical training are better indicators of failure. Yet, there are medical students who fail Step 1 of the USMLE who enter medical school with high MCAT scores, and conversely medical students with lower academic credentials who are expected to have difficulty passing Step 1 but pass on the first attempt. Researchers have attempted to find the factors associated with Step 1 outcomes; however, there are two problems associated with their methods used. First is the small sample size due to the high national pass rate of Step 1. And second, research using multivariate regression models indicate correlates of Step 1 but does not predict individual student performance. This study used data mining methods to create models which predict medical students at risk of failing Step 1 of the USMLE. Predictor variables include those available to admissions committees at application time, and final grades in courses taken during the preclinical years of medical education. Models were trained, tested, and validated using a stepwise approach, adding predictor variables in the order of courses taken to identify the point during the medical education continuum which best predicts students who will fail Step 1. Oversampling techniques were employed to resolve the problem of small sample sizes. Results of this study suggest at risk medical students can be identified as early as the end of the first term during the first year. The approach used in this study can serve as a framework which if implemented at other U.S. allopathic medical schools can identify students in time for appropriate interventions to impact Step 1 outcomes

The purpose of this study is to empirically investigate the occurrence of Step 1 measures ofthe “four step principle” and the role of Strategic Choice of Measures (SCM) as it pertains tothe development of a sustainable transport system. The “four step principle” has been a partof Swedish transport planning for the last two decades with the purpose of modernising it. Byimplementing SCM the “four step principle” was supposed to take a central role in planning.SCM was supposed to offer an earlier and more open process to make strategic, nationaltransport planning more efficient and enable new perspectives in the process. However, newperspectives are few and far between and it is questionable how sustainable the measuressuggested in SCM processes actually are. The step-wise examination of the “four stepprinciple” seldom leads to suggestions where Step 1 measures are a considerable part, andit is even unclear what Step 1 actually is. Going beyond the SCM process it is questionable ifthe Measure data bank (Åtgärdsbanken), where all SCM-recommended measures aresupposed to be registered, works as a data and knowledge management database.This thesis steps in at that point.The goal of this report is to explain what Step 1 actuallyrepresents, how this is expressed in the SCM methodology and what the output of the SCMprocess is. By text analysis of documents from 150 SCM processes with possible Step 1measures an examination is made of what Step 1 represents and how Swedish TransportAdministration and other participants in SCM processes reach the measures suggested.What kind of so called primary Step 1 measures that are generated in the SCM process isalso within the scope of this report. In addition a more detailed investigation of patterns isperformed for SCM processes that generate primary Step 1 measures, pertaining to thedelimitation and actor setup in SCM:s. This is done in order to answer questions surroundingthe process of generating measures in SCM is concerned, and also to attempt to answer thecriticism that has been brought forth concerning the SCM process.The main conclusion of this report is that there are clear flaws in the SCM process. Theseshortcomings and flaws include difficulties in classifying measures, that particularly Step 1measures are packaged without individual effect evaluation, and that the process at largelacks the “rethink” perspective which the SCM process aims at. Looking at the primary Step1 measures generated, they are characterised as having a time scope connected to otherplanning processes, a limited, local geographical expanse, and as being multi-modal. Theresearch shows a contrast in that certain primary Step 1 measures occur at particulargeographical delimitations. Two common conditions for the generation of primary Step 1measures are the exclusion of Step 4 in the generation of measures, and packeting of Step1 measures for ease of handling. When investigating the SCM process the criticism that isbrought forth towards it shows signs of being legitimate. At the same time it is clear the theMeasure data bank does not function correctly as a data and knowledge management bankand improvements are needed for it to serve its intended purpose.Keywords: “four step principle”, Strategic Choice of Measures, sustainable transport,transport planning, Measure data bank, actor, Step 1, Primary Step 1 measure, instrument.