![]() Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible. The command set.seed(20190110) was run prior to running the code in the R Markdown file. For reproduciblity it’s best to always run the code in an empty environment. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. Great job! The global environment was empty. Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results. In the future, it will be important for pharmacy educators to identify and study best practices for use of PCOA within student assessment and remediation plans.Last updated: workflowr checks: (Click a bullet for more information) Survey results indicate wide variability between programs regarding PCOA cut points (benchmarks), stakes, and remediation approaches. ![]() Remediation was most commonly required for P3 students (22%).Conclusion. Remediation for at risk students was required by less than 25% of programs. Programs used a variety of approaches to establish the benchmark (or cut point) for PCOA performance. The approach for assigning “stakes” to PCOA performance varied among programs depending on the student’s professional year in the curriculum. The PCOA was most frequently administered to third-year pharmacy (P3) students. The most common uses of the PCOA included curricular assessment (76%), individual student performance assessment (74%), and cohort performance assessment (71%). Survey items were designed to investigate common uses of the PCOA, cut points, and “stakes” assigned to the PCOA, identification of at-risk students, and remediation approaches.Results. Assessment professionals from 135 US schools and colleges of pharmacy were invited to complete a 38-item electronic survey. ![]() To determine and describe the current uses of the Pharmacy Curriculum Outcomes Assessment (PCOA) by US schools and colleges of pharmacy.Methods. Other physician assistant programs could develop analyses of their internal testing assessments to identify students at risk of PANCE failure and allocate institutional and program resources to improve rates of PANCE passage. Most importantly, regression analyses revealed the combined solution of SUMM and MCQ to most accurately predict PANCE performance at the low end of the scale.CONCLUSION: This research suggests that all assessment measures tested can provide helpful estimates of PANCE performance however the combined SUMM and MCQ solution provided the most reliable and accurate prediction of PANCE performance for "high risk" students. Discriminant analyses revealed the combined solution of PACKRAT, SUMM, and MCQ to most accurately differentiate between the "pass" versus "fail" groups on the PANCE. This study's goal was to improve prediction of PANCE failure using other predictive measures, alone and in combination, with the PACKRAT.METHODS: Correlation and discriminate and regression analyses were conducted on 3 years of data (2007-2009) collected from Chatham University PA Studies graduates, with convergent results.RESULTS: Significant positive correlations between the PANCE and each of the assessment measures were found (in order of significance): (1) the PACKRAT exam (2) a summative multiple choice question exam given near the end of the clinical (second) year (3) average multiple-choice-question-exam results from the didactic (first) year (4) prerequisite preadmission GPA and (5) overall preadmission GPA. PURPOSE: A significant positive correlation between the Physician Assistant National Certifying Examination (PANCE) and the PAEA Physician Assistant Clinical Knowledge Rating and Assessment Tool (PACKRAT) has been found in previous studies. ![]()
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