Quality improvement in physical therapy education: What contributes to high first-time pass rates on the National Physical Therapy Examination?

Quality improvement in physical therapy education: What contributes to high first-time pass rates on the National Physical Therapy Examination?

Date: May 2001
Creator: Palmer, Phillip B.
Description: The purposes for this study were: (a) to establish benchmark metrics for selected variables related to characteristics of physical therapy education programs; and (b) to determine how well a subset of the variables predicted group membership based on first-time pass rates (FTPRs) on the National Physical Therapy Examination (NPTE). The population was defined as all physical therapy programs in the United States and Puerto Rico accredited by the Commission on Accreditation in Physical Therapy Education. Questionnaires soliciting data related to the variables were mailed to the entire population (N = 177). Fifty-eight (32.8%) of the programs returned the questionnaire, with 51 (29%) having provided enough information for inclusion in the study. Characteristics of the sample were compared to known population characteristics in order to determine the extent to which the sample represented the population. Pearson product-moment correlation resulted in a coefficient of .993, indicating that the two groups were similar. Descriptive statistics were calculated. Values for the variables were tabulated in various ways, based on the nature of sponsoring institution, regional location, degree offered, and grouping based on FTPRs, in order to facilitate comparisons. A single institution was selected and comparisons made to demonstrate the utilization of benchmark metrics. Chi-squared ...
Contributing Partner: UNT Libraries
CLUE: A Cluster Evaluation Tool

CLUE: A Cluster Evaluation Tool

Date: December 2006
Creator: Parker, Brandon S.
Description: Modern high performance computing is dependent on parallel processing systems. Most current benchmarks reveal only the high level computational throughput metrics, which may be sufficient for single processor systems, but can lead to a misrepresentation of true system capability for parallel systems. A new benchmark is therefore proposed. CLUE (Cluster Evaluator) uses a cellular automata algorithm to evaluate the scalability of parallel processing machines. The benchmark also uses algorithmic variations to evaluate individual system components' impact on the overall serial fraction and efficiency. CLUE is not a replacement for other performance-centric benchmarks, but rather shows the scalability of a system and provides metrics to reveal where one can improve overall performance. CLUE is a new benchmark which demonstrates a better comparison among different parallel systems than existing benchmarks and can diagnose where a particular parallel system can be optimized.
Contributing Partner: UNT Libraries