Novel use of a sequential standardized oral board exam to assess PGY1 resident progression

Purpose A standardized oral board exam was created to longitudinally assess postgraduate year 1 (PGY1) pharmacy residents in key domains. Summary We provide a descriptive review of a novel oral board exam administered quarterly to our PGY1 pharmacy residents. Preceptors from our core rotations (internal medicine/infectious diseases, adult critical care, oncology, pediatrics, and administration/health policy and outcomes) developed questions based on situations commonly encountered by PGY1 residents to assess residents’ communication; the content of their response, assessment, and plan; and coachability. Over the 4-year history of this assessment, scoring has matured to consider whether a resident has or has not met or has exceeded expectations for a PGY1 resident at a given stage in their training. Our comprehensive feedback and action planning approach included residents’ self-assessment, feedback from the exam committee, development and implementation of a customized training plan for execution, and dissemination to our preceptors. Systematically assessing our PGY1 residents with this innovative method provided a process for tracking their performance and served as a baseline for those who completed additional training at our institution. Conclusion A standardized quarterly oral board exam was developed to identify residents’ strengths and areas for improvement at established periods during the PGY1 residency training program. This standardized assessment, paired with individualized action plans and open communication with key stakeholders, stimulated development in residents’ performance, communication, and interpersonal skills. We aim to expand this system’s application to identify predictors of success for candidates we interview for our postgraduate training programs.

On the Development of a Personalized Augmented Reality Spatial Ability Training Mobile Application

Augmented Reality has been integrated in educational settings in the field of engineering. Prior research has examined the learning outcomes and the pedagogical affordances of this technology. However, training undergraduate engineers, from diverse knowledge level, requires customized training approach, tailored to the individual learning pace. In this paper, we present PARSAT (Personalized Augmented Reality Spatial Ability Training), which is a mobile Augmented Reality application for the enhancement of students’ spatial visualization skills. The application takes into account the theoretical contents of engineering design, deployed through video tutorials, and student-computer interaction with 3D objects. Students interpret different views of a 3D object, which are represented on their mobile screen. PARSAT efficaciously strengthens students’ recognition of spatial structures and views, adjusted to the fulfillment of their personal needs. In terms of personalization, PARSAT consists of different levels, which do not follow a linear flow, as each student takes part in a different sequence of activities, according to their time spent in the 3D object manipulation, and their assessment scores at the end of each level. Furthermore, an agent is used to analyze students’ knowledge level, and send them feedback. The system reduces unnecessary cognitive load and, at the same time, improves students learning experience in learning engineering drawing.

From "sigma test" to customized training

This article illustrates how you can assign a metabolic exercise to a tennis player, after having performed the "Sigma Test" and having acquired the subjective parameters.

Hong Kong as public administration metropolis in the internationalization of public administration education: one Hong Kong university experience

PurposeThis article explores the roles and the expertise of Hong Kong in the internationalization of public administration education.Design/methodology/approachThe methodology is based on the data of 5 internationalization initiatives of one Hong Kong university with its internationalization partners in Macau, Korea, Australia, Russia and Finland. The data obtained lasted for a period of 18 months, from September 2019 to March 2021.FindingsThe finding of this study revealed that (1) there are 5 “pubtropolis roles” (roles of a public administration metropolis) of Hong Kong in the internationalization of public administration education in China, Asia, Asia-Pacific, Belt-and-Road and Europe. The findings also revealed that (2) Hong Kong served as a pubtropolis with its “5C” expertise in curriculum innovation, customized training, competence framework, competence assessment and comparative policy.Research limitations/implicationsAs the methodology of this article is based on the data of 5 internationalization initiatives of one Hong Kong university by one academia only, further studies can be conducted at department, faculty or university level for multiple academia.Practical implicationsThere are two practical implications: (1) The more the roles of a city, the broader the view in its internationalization of public administration initiatives; (2) Hong Kong could further tap on its expertise in “5C” in public administration: curriculum innovation, customized training, competence framework, competence assessment and comparative policy to exert its “geo-management” power.Social implicationsThis article argues that public services can be improved by the setting up of “Sabbatical Leave Scheme for Internationalization of Public Administration” by respective governments to sustain the impacts observed.Originality/valueIt is from the author's original work.

Study of Image Classification Accuracy with Fourier Ptychography

In this research, the accuracy of image classification with Fourier Ptychography Microscopy (FPM) has been systematically investigated. Multiple linear regression shows a strong linear relationship between the results of image classification accuracy and image visual appearance quality based on PSNR and SSIM with multiple training datasets including MINST, Fashion MNIST, Cifar, Caltech 101, and customized training datasets. It is, therefore, feasible to predict the image classification accuracy only based on PSNR and SSIM. It is also found that the image classification accuracy of FPM reconstructed with higher resolution images is significantly different from using the lower resolution images under the lower numerical aperture (NA) condition. The difference is yet less pronounced under the higher NA condition.

Research trends in computerized cognitive training contents with text network

Computerized Cognitive Training (CCT) contents used to improve patients’ cognitive ability with Mild Cognitive Impairment (MCI) can provide customized training through individual data collection and analysis. However, studies on transfer effect of improving other untrained cognitive domains while performing the contents are insufficient. The present paper intended to collect literature published by PubMed, EMBASE, Cochrane Library, and Web of Science until December 2019 and analyze the trends of CCT and the transfer effect in each training area. Studies on CCT (82/891) have been increasing each year, and universities (60/82) in the United States (17/82) have published the most. In the literature that reported clinical effect (18/82), the cognitive domain mostly studied was memory (14/18), and the N-Back (3/14) method accounted for most of the training contents. Moreover, the contents that showed the highest degree, closeness, and betweenness centrality (BC) indices were the memory area, and video accounted for the highest among the intervention methods. In particular, the closeness centrality (CC) index of the memory and attention contents showed similar results. It can be interpreted that the possibility of the transfer effect occurring from memory and attention areas is the highest since the semantic distance (i.e. the similarity of the training process) between the attention contents and memory contents was the closest. The effectiveness of the actual transfer effect between the memory and attention should be verified.

Maximal Strength, Sprint, and Jump Performance in High-Level Female Football Players Are Maintained With a Customized Training Program During the COVID-19 Lockdown

IntroductionThe COVID-19 outbreak with partial lockdown has inevitably led to an alteration in training routines for football players worldwide. Thus, coaches had to face with the novel challenge of minimizing the potential decline in fitness during this period of training disruption.MethodsIn this observational pre- to posttest study involving Norwegian female football players (18.8 ± 1.9 years, height 1.68 ± 0.4 m, mass 61.3 ± 3.7 kg), we investigated the effects of a prescribed home-based and group-based intervention, implemented during the COVID-19 lockdown, on maximal muscular force production and high velocity variables. Specifically, maximal partial squat strength one repetition maximum (1RM), counter movement jump (CMJ) and 15 m sprint time were assessed 1 week prior to the lockdown and 12 weeks after the onset of lockdown. We also collected training content and volume from the prescribed training program and self-reported perceived training quality and motivation toward training.ResultsWe observed no change in 1RM [pretest: 104 ± 12 kg, posttest: 101 ± 11 kg (P = 0.28)], CMJ height [pretest: 28.1 ± 2.3 cm, posttest: 26.8 ± 1.9 (P = 0.09)], and 15 m sprint time [pretest: 2.60 ± 0.08 s, posttest: 2.61 ± 0.07 s (P = 0.52)].ConclusionOur findings suggest that a prescribed home-based and group-based intervention with increased training time devoted to strength, jump, and sprint ability, and regulated to obtain a sufficient infection control level is feasible and effective to preserve strength, jumping, and sprinting abilities of high-level female football players during a ∼ 3-month period of a pandemic-induced lockdown.

STVALL: Hybrid TV for Interactive Language and Content Learning

Hybrid television refers to the merging of the Internet and traditional television via a multi-user platform. In this scope, we have developed the STVALL project for the past two-three years on a regional scale (Extremadura TV in Spain). This technology aims to provide an educational platform for interactive and adaptive (individual or group) learning of content and language via the smart television. Our research group has focused on the development of specific activities and challenges (so-called customized training pills) to feed content and information into the authoring tool, which stores and distributes it from its knowledge base. As education experts (language and content teachers / educators), we have labelled this content according to five subject areas (Science and Nature, Literature and Art, Geography and History, Entertainment and Sports, and Language) and four language user levels: Adult (over 12 years of age) / Children (0-12): A1/A2/B1/B2. In addition, the content has been assigned other types of tags for user-related feedback in the authoring tool (e.g., monologic vs. dialogic, narrative vs. instructions, etc). Thus, upon interaction with the program, users build a content and language level profile that the system will store and remember for the next interaction (single- or group-based). Because the users’ profiles may differ significantly, this system has been tested with groups of adults and children so that their specific aims and inclinations as regards content and language learning can be registered and compared. By relying on users’ performance and personal surveys, our team will be able to specify more customized types of activities, some of which require experts’ responses and mediation.

Association between Cigarette Smoking and Physical Fitness Level of Korean Adults and the Elderly

Although previous studies have examined the relationship between smoking and physical fitness, they only considered current smoking status and the same fitness measurements regardless of age. This study investigated differences in physical fitness based on tobacco smoking habits. A total of 2830 non-elderly adults (NEA; 19–64 years) and 629 elderly (65–89 years) participated in the study, using data extracted from a Korean national database. One-way ANCOVA and ANOVA were conducted to analyze the results. The subjects were classified into three groups (smokers, those who had quit, and never-smokers). In NEA men, a significant difference was observed in 50-m dash (p = 0.003) and 20-m shuttle-run (p < 0.001), while in elderly men differences were only seen in sit-ups (p = 0.015). In the case of NEA and elderly women, no significant differences were observed in physical fitness levels (p > 0.05). The decreased fitness level due to smoking was more noticeable in men than in women, and in NEA more than in elderly persons. A non-smoking policy and customized training based on age or gender are necessary to increase fitness and improve health conditions.

Nominate of significant features for unknown internet traffic applications filtering based on a neural network algorithm

The evolution of the internet into a large, complex service-based network has posed tremendous challenges for network monitoring and control in terms of how to collect massive volumes of data, in addition to the accurate classification of new emerging applications, such as peer-to-peer networks, streaming content and online games. In this work, machine learning algorithms are used for the classification of traffic into their corresponding applications. Furthermore, this research uses our customized training data set collected from the three institutions' campuses. The effect on the size of the training data set has been considered before examining the accuracy of various classification algorithms and selecting the best from a large amount of data traffic in the network, which has led to delays in performance; therefore, to solve this problem we suggested a distinct approach using multiple neural networks with the feature selection in order to predict and identify known and unknown applications. By applying the proposed method, we get excellent accuracy in the classification of data traffic in the network of up to 99.11%, which leads to improved data traffic in the network and avoids delays.