An analysis of the selection criteria for postgraduate physician assistant residency and fellowship programs in the United States

Background This study aims to investigate the admission criteria used by physician assistant postgraduate education programs in selecting licensed PA applicants for postgraduate training in the United States. To our knowledge, there have been no previously published reports on selection criteria and/or other factors influencing postgraduate PA admission decisions. Method A non-experimental, descriptive research study was designed to obtain information from members of the Association of Postgraduate Physician Assistant Programs (APPAP). Results Twenty-three out of 73 postgraduate programs (35%) responded to the survey. The study reported that applicant PAs and NPs are largely selected on the basis of several factors. The most heavily weighted factor is the interview itself; other selection criteria perceived to be extremely/very important included board certification/eligibility, letters of recommendation, advanced degree, and personal essay. Survey data suggest that publications, undergraduate transcripts, and class rankings are not considered to be of high importance in applicant selection. The number of PA applicants applying to each postgraduate training program averages around 26 and total number of enrollees is about 3.6 per program. Additionally, some programs reported furloughing of trainees (temporary suspension of didactic and clinical training) during the pandemic, whereas the vast majority of postgraduate PA programs remained operational and some even experienced an increase in application volume. The total cost of training a PA resident or fellow in postgraduate programs is currently $93,000 whereas the average cost of training a categorical physician resident is estimated at $150,000 per year when considering both salary and benefits. Conclusions This novel study examined criteria and other factors used by postgraduate PA programs in selecting candidates for admission. Results can be used by postgraduate programs to improve or modify current selection criteria to enhance the quality of trainee selection. Further research is needed to examine correlations between applicant attributes, selection criteria, and trainee success in completing postgraduate training.

Image Enhancement Using Weighted Bi-Histogram Equalization

Image enhancement is one of using in various digital signal processing areas. Advances in microcontrollers, microcomputers and computers have developed traditional algorithms in order to improve the quality of the resulting image and have implied many avenues to the design of new innovations using various techniques. This paper proposes contrast enhancement using weighted bi-histogram equalization based on distributed area ratio. Moreover, this technique must use a weighted factor which is calculated by the ratio of the histogram distribution. Likewise, an original image will be equalized by the modification of the probability density function of the gray levels. As result of the experiment, the contrast of resulting image is improved for implement and human perception – as well as also reduces the absolute mean brightness error (AMBE) than the traditional technique of image enhancement.

Introducing a bibliometric index based on factor analysis

This work applies a factor analysis with VARIMAX rotation to develop a bibliometric indicator, named the Weighted Factor Index, in order to derive a new classification for journals belonging to a certain category, alternative to the one provided by the Journal Impact Factor. For this, 16 metrics from three different databases (Web of Science, Scopus and SCImago Journal Rank) are considered. The Weighed Factor Index entails the advantage of incorporating and summarizing information from all the indicators; so as to test its performance, it was applied to rank journals belonging to the category Information Science & Library Science.

An Analysis of the Selection Criteria for Postgraduate Physician Assistant Residency and Fellowship Programs in the United States

BackgroundThis study aims to investigate the admission criteria used by PA postgraduate education programs in selecting licensed PA applicants for postgraduate training in the United States. To our knowledge, there have been no previously published reports on selection criteria and/or other factors influencing postgraduate PA admission decisions. This study both draws on and builds upon previous research conducted by Vasco Deon Kidd et. al in exploring the characteristics of PA postgraduate education programs in the United States.Method A non-experimental, descriptive research study was designed to obtain information from members of the Association of Postgraduate Physician Assistant Programs (APPAP). ResultsTwenty-three out of 73 postgraduate programs (35.1%) responded to the survey; the low response rate in our survey may have been attributed to the resurgence of COVID-19 fueled by the delta variant. Nevertheless, the study reported that applicant PAs and NPs are largely selected on the basis of several factors. The most heavily weighted factor is the interview itself; other selection criteria perceived to be extremely/very important included board certification/eligibility, letters of recommendation, advanced degree, and personal essay. Survey data suggest that publications, undergraduate transcripts, and class rankings are not considered to be of high importance in applicant selection. The total cost of training a PA resident or fellow in postgraduate programs is currently $93,000 whereas the average cost of training a categorical physician resident is estimated at $150,000 per year when considering both salary and benefits. The number of PA applicants applying to each postgraduate training program averages around 26 and total number of enrollees is 3.6 per program.ConclusionsThis is the first study to examine criteria and others factors used by postgraduate PA programs in selecting candidates for admission. Results can be used by postgraduate programs for quality improvement initiatives related to including additional or modifying current selection criteria to improve the quality of trainee selection. Further research is needed to examine correlations between applicant attributes, selection criteria, and trainee success in completing postgraduate training.

PP17 Development and application of a clinical risk stratification tool using routine data from electronic patient clinical records

BackgroundFollowing the introduction of electronic patient clinical records, ambulance service managers wished to combine clinical and operational data to devise a method of risk stratifying 999 calls by the MPDS disposition code assigned at call triage. Initial aims were to establish the risk threshold if an ambulance was no longer routinely dispatched.MethodsData selected were representative of high or low clinical risk, and reliably recorded. The following ‘risk factors’ were chosen:Call outcomeEmergency conditionsClinical interventionsMedications administeredWith expert local opinion, a scoring algorithm was created using weighted factor scores to create an aggregate risk score for each MPDS code. It was also designed to distribute codes along a ‘risk range’, allowing for thresholds setting suitable to the specific purpose of individual projects. These factors and their scores were captured alongside contextual information and to date contains over 1.4 million records over 3 years.In collaboration with academic colleagues, we also developed an AI model to refine the algorithm used to reflect acuity. With one year of data the tool did not demonstrate the sensitivity or specificity to reliably contribute to prediction, however this exercise may be repeated now there is a greater volume of data.Applications: This Tool has been successfully used for a variety of purposes:Developing the Enhanced Hear and Treat policyAssessing risk of code downgrades in the pandemic responseIdentifying codes suitable for automatic specialist clinician allocationsSupplementing analysis of harm caused by long response delaysIdentifying codes for protection within End of Shift protocolsProviding intelligence to aid national decisions on code categorisationNext steps: The Tool continues to assist in decision-making locally. Future ambitions include:Validation of the scoring algorithmsProcess automation to ensure more timely data is availableCollaboration to improve the variety and volume of data

Total variation with modified group sparsity for CT reconstruction under low SNR

BACKGROUND AND OBJECTIVE: Since the stair artifacts may affect non-destructive testing (NDT) and diagnosis in the later stage, an applicable model is desperately needed, which can deal with the stair artifacts and preserve the edges. However, the classical total variation (TV) algorithm only considers the sparsity of the gradient transformed image. The objective of this study is to introduce and test a new method based on group sparsity to address the low signal-to-noise ratio (SNR) problem. METHODS: This study proposes a weighted total variation with overlapping group sparsity model. This model combines the Gaussian kernel and overlapping group sparsity into TV model denoted as GOGS-TV, which considers the structure sparsity of the image to be reconstructed to deal with the stair artifacts. On one hand, TV is the accepted commercial algorithm, and it can work well in many situations. On the other hand, the Gaussian kernel can associate the points around each pixel. Quantitative assessments are implemented to verify this merit. RESULTS: Numerical simulations are performed to validate the presented method, compared with the classical simultaneous algebraic reconstruction technique (SART) and the state-of-the-art TV algorithm. It confirms the significantly improved SNR of the reconstruction images both in suppressing the noise and preserving the edges using new GOGS-TV model. CONCLUSIONS: The proposed GOGS-TV model demonstrates its advantages to reduce stair artifacts especially in low SNR reconstruction because this new model considers both the sparsity of the gradient image and the structured sparsity. Meanwhile, the Gaussian kernel is utilized as a weighted factor that can be adapted to the global distribution.

Perbandingan Metode AHP dan Metode SAW Dalam Kelayakan Pemberian Kredit Motor

In this study, two methods were used to determine the feasibility of giving motorbike credit, namely the Analytical Hierarchy Process (AHP) and the Simple Additive Weighting (SAW) method to determine the weight of the accuracy value in the feasibility of granting motor loans. Results based on the Hierarchical Weighted Factor Matrix with AHP for all criteria normalized hierarchical weighting for all criteria with the elements in each column divided by the total number in the respective column, then you will get the normalized relative weight. The eigenvector value generated from the average relative weight value for each row shows that the most important criterion for customers who wish to apply for credit. Income with a weight of 0.649 or 64.9%, then followed by a family card with a weight of 0.088 or 8.8%, and domicile is 0.21 or 21%. Whereas the results based on ranking using the SAW method for all Kritera whose weighting is normalized is that the V1 ranking is the first rank because it has a value greater than the other values of 1.03 where V1 is the preference value of alternative A1, so that A1 in this case is Yogi Danuarta who be the best alternative or selected prospective customers to get motorbike loans.