The challenges of models-based practice in physical education teacher education: a collaborative self-study

There are two purposes of this study. The first is to examine our experiences as beginning teacher educators who taught using models-based practice (using the example of Cooperative Learning). The second is to consider the benefits of using collaborative self-study to foster deep understandings of teacher education practice. The findings highlight the challenges in adapting school teaching practices to the university setting, and the different types of knowledge required to teach about the “hows” and “whys” of a models-based approach. We conclude by acknowledging the benefits of systematic study of practice in helping to unpack the complexities and challenges of teaching about teaching. Our collaborative self-study enabled us to develop insights into the intertwined nature of self and practice, and the personal and professional value of our research leads us to encourage teacher educators to examine and share their challenges and understandings of teaching practice

Pulmonary Rehabilitation in COPD: Current Practice and Future Directions

This chapter will review the rationale for and the need for pulmonary rehabilitation in patients with Chronic Obstructive Pulmonary Disease (COPD). Its clinical effectiveness will be considered, including the evidence supporting a role for rehabilitation in improving exercise tolerance in COPD as measured. While the influence of pulmonary rehabilitation on dyspnoea, exercise tolerance and quality-of-life is clear, evidence for the benefits of rehabilitation on reducing healthcare utilisation such as admission to hospital or attendance at out-of-hours services is limited. The chapter will provide guidance on the setting up of a pulmonary rehabilitation programme and clinical staff required and the suitability of patients to enter such programmes will be outlined. There will be discussion on the key components of a programme including education, nutritional advice and the management of dyspnoea. Exercise is the central component of pulmonary rehabilitation. Assessment of the patient and prescription of an exercise programme will be outlined as will assessing a patient’s improvement. One key goal of pulmonary rehabilitation is ongoing lifestyle modification to encourage patients to undertake a more active lifestyle in the future. Ways of activating patients to do this will be discussed and the evidence for the use of telehealth in this area will be reviewed

Watching Hands in the Cookie Jar: A Project for Improving Medication Charge Capture in the Operating Room

Introduction: With the high cost of healthcare in the United States, there have been attempts to identify waste (1). In our anesthesia department, vials or syringes of non-controlled medications are not checked out individually from a pharmacist. Rather, they are removed from a medication housing machine (BD Pyxis Anesthesia Station ED™) before and during a case based on an honor system. The provider logs into the Pyxis system and then has access to any medication in that reservoir. When an individual medication is removed, a button has to be pressed on a screen to register its removal. Anyone who has electronic access to the Pyxis system may log in and withdraw a medication, potentially without documenting the removal. Our current system tracks medication use based on those documented as being removed from the Pyxis, and thus if a medication has not been “checked out”, it will not be billed. Feedback and Audit is a tool that can be utilized to change provider behavior (3) and potentially decrease medication discrepancies. Below, we describe the results of implementing such a tool, evaluating change in behavior, and the cost savings of doing so. Problem: For the calendar year of 2018, there were 20,421 cases performed with an estimated loss per case of $11.49. There is currently no incentive for correct practice, nor repercussion for failing to “check out” a medication from the Pyxis machine. Our pharmacy department follows these costs and losses, and has provided feedback to determine overall lost revenue. Methodology: Using the electronic anesthesia record, all non-controlled medications that were documented as administered to the patient were analyzed, regardless of amount of medication that was administered. Any provider who was documented as a care provider on the chart was listed as a person that may have administered the drug. We included cases performed in the operating rooms of the main hospital, labor and delivery, BBRP (pediatric), and the Outpatient Surgical and Imaging Center (OSIS) for a total of 31 anesthetic sites. Exclusion criteria included emergency cases (ASA class “E”), controlled medications, as well as anesthetics provided in sites other than those listed above (e.g. SRMC or CCOR). Data was extracted using name-identified information. Using the average wholesale price (AWP) of the medications for the month of September 2019, we calculated the estimated lost revenue. We then calculated the mean percentage of medication dispensing discrepancy, and compared this with the habits of all anesthesia providers. Starting in January of 2019, this information was published on a slideshow that is displayed on an electronic announcement board in the anesthesiology break room, enabling providers to see how their practices compared with their peers’. After three months, there was a plateau in behavior. The residency director and director of the Advanced Practice Providers were then asked to send individual emails to the providers whose compliance was two standard deviations below the mean behavior in an attempt to improve compliance. Results: For the calendar year of 2018, there were 20,421 cases performed in the above-mentioned operating rooms. The estimated loss per case was$11.49. For the months of November and December of 2018, there was an average provider baseline compliance rate of 77.76% for checking out medications. For the calendar year 2019, there were 21,290 cases performed in those operating rooms, with an estimated loss per case of $5.06. From November to December of 2019, there was an average provider compliance rate of 82.16$127,000. Discussion: It is estimated that the cost of United States healthcare approaches 18% gross domestic product, and up to 30% of this may be waste (2). In the field of anesthesiology, other institutions have looked at ways to reduce waste and overall expenditure (1), and have attempted to utilize a similar Feedback and Audit tool (5). Changing provider behavior can be difficult. Some of the most effective methods for changing behavior can also be the most effective (3). A Cochrane review looking at the effects on professional practice when using an Audit and Feedback system showed a median risk difference of 4.3% (4). Using an electronic slideshow with the published names of anesthesia providers and their compliance rates, we were able to demonstrate a similar improvement of dispensing practice of 4.4%, which correlated to a cost savings of $6.54 per case, averaged over 21,290 cases, and an estimated calendar-year cost savings of$127,000. It is interesting to note that this degree of improvement correlated with a cost savings of 38.9%. One possible explanation for the initial poor compliance rate of 77.76% is the design of the Pyxis system, and the need for multiple steps to register removal of a medication. This problem would best be addressed from a machine that was designed to follow expected provider behavior and workflow naturally. Limitations of this analysis include unintentional exclusion of certain medications that are commonly utilized, as well as including medications that are not stored in the Pyxis dispenser. Limitations to this review include sustainability, as one institution that utilized an Audit and Feedback tool demonstrated a downward trend in compliance in the post-intervention time frame (5). References: Rinehardt E, Sivarajan M. Costs and wastes in anesthesia care. Current Opinion in Anaesthesiology. 2002;25(2):221-225 Chrank W, Rogstad T, Parekh N. Waste in the US Health Care System: Estimated Costs and Potential for Savings. Journal of the American Medical Association. 2019;322(15):1501-1509 Trowbridge R, Weingarten S. Making Health Care Safer: A Critical Analysis of Patient Safety Practices. Chapter 54. Educational Techniques Used in Changing Provider Behavior. https://archive.ahrq.gov/clinic/ptsafety/chap54.htm Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD, O\u27Brien MA, Johansen M, Grimshaw J, Oxman AD. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database of Systematic Reviews 2012, Issue 6. Bowdle T, Jelacic S, Nair B, et al. Improve Anesthesia Provider Compliance with a Barcode-Based Drug Safety System. Anesthesia and Analgesia. 2019:129(2)418-425

Recognizing and Extracting Cybersecurtity-relevant Entities from Text

Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a strong need to develop community-accessible datasets to train existing AI-based cybersecurity pipelines to efficiently and accurately extract meaningful insights from CTI. We have created an initial unstructured CTI corpus from a variety of open sources that we are using to train and test cybersecurity entity models using the spaCy framework and exploring self-learning methods to automatically recognize cybersecurity entities. We also describe methods to apply cybersecurity domain entity linking with existing world knowledge from Wikidata. Our future work will survey and test spaCy NLP tools and create methods for continuous integration of new information extracted from text

DockoMatic - Automated Ligand Creation and Docking

Background: The application of computational modeling to rationally design drugs and characterize macro biomolecular receptors has proven increasingly useful due to the accessibility of computing clusters and clouds. AutoDock is a well-known and powerful software program used to model ligand to receptor binding interactions. In its current version, AutoDock requires significant amounts of user time to setup and run jobs, and collect results. This paper presents DockoMatic, a user friendly Graphical User Interface (GUI) application that eases and automates the creation and management of AutoDock jobs for high throughput screening of ligand to receptor interactions. Results: DockoMatic allows the user to invoke and manage AutoDock jobs on a single computer or cluster, including jobs for evaluating secondary ligand interactions. It also automates the process of collecting, summarizing, and viewing results. In addition, DockoMatic automates creation of peptide ligand .pdb files from strings of single-letter amino acid abbreviations. Conclusions: DockoMatic significantly reduces the complexity of managing multiple AutoDock jobs by facilitating ligand and AutoDock job creation and management

Estimating Rumen Undegradable Protein in

An in situ trial was conducted to compare estimates of rumen undegradable protein (UIP) using a single incubation time point and rates of degradation. Four forage samples (three legumes and one grass) were incubated in situ for their mean retention time estimated from in vitro dry matter disappearance plus a 10-hour lag time as well as for a time point equal to 75% of the total mean retention time (mean retention time plus lag). The UIP values obtained from the fractional rates of degradation and passage were more highly correlated with those estimated from 75% of the total mean retention time (R2 = 0.99) than those estimated from the total mean retention time (R2 = 0. 62). The UIP of birdsfoot trefoil was higher than that in the other forages