The case of veterinary interprofessional practice: From one health to a world of its own

BACKGROUND: Research regarding the veterinary professions' involvement in interprofessional practice and education (IPE), either with health care professionals as part of One Health, or specifically within the veterinary health care team, is sparse. PURPOSE: To investigate veterinary interprofessional working and learning in veterinary practices; then ultimately to make recommendations for IPE. METHOD: Two case studies in typical but contrasting practices were conducted. The study consisted of three sequential and complementary weeks: 1) observing the whole team, 2) shadowing selected focus individuals from each profession and 3) interviewing focus individuals regarding teamwork. Triangulation was achieved by synthesis of emergent themes from observational field notes and interview transcripts. DISCUSSION: Facilitators to interprofessional practices included hierarchy, trust and value, different perspectives, formal infrastructure and professionalization. Challenges included hierarchy, spatial and temporal work patterns, professional motivations, and error and blame. CONCLUSION: The veterinary and human health care fields face similar interprofessional challenges. Real life observations, as described here, can provide important insight relevant to the design of IPE initiatives

The winding path to a PhD in veterinary education

Masters and PhD degrees specific to veterinary education are relatively novel, but the number of students in this area is growing. As two current students, Tierney Kinnison and Sylvain Dernat, explain, those undertaking these degrees have vastly different backgrounds and are researching a variety of topics. By sharing the experiences of those involved, they hope to encourage the next generation of veterinary educators to begin their research careers

Errors in Veterinary Practice: Preliminary Lessons for Building Better Veterinary Teams

Case studies in two typical UK veterinary practices were undertaken to explore teamwork, including interprofessional working. Each study involved one week of whole team observation based on practice locations (reception, operating theatre), one week of shadowing six focus individuals (veterinary surgeons, veterinary nurses and administrators) and a final week consisting of semistructured interviews regarding teamwork. Errors emerged as a finding of the study. The definition of errors was inclusive, pertaining to inputs or omitted actions with potential adverse outcomes for patients, clients or the practice. The 40 identified instances could be grouped into clinical errors (dosing/drugs, surgical preparation, lack of follow-up), lost item errors, and most frequently, communication errors (records, procedures, missing face-to-face communication, mistakes within face-to-face communication). The qualitative nature of the study allowed the underlying cause of the errors to be explored. In addition to some individual mistakes, system faults were identified as a major cause of errors. Observed examples and interviews demonstrated several challenges to interprofessional teamworking which may cause errors, including: lack of time, part-time staff leading to frequent handovers, branch differences and individual veterinary surgeon work preferences. Lessons are drawn for building better veterinary teams and implications for Disciplinary Proceedings considered

SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization

Computer vision is experiencing an AI renaissance, in which machine learning models are expediting important breakthroughs in academic research and commercial applications. Effectively training these models, however, is not trivial due in part to hyperparameters: user-configured values that control a model's ability to learn from data. Existing hyperparameter optimization methods are highly parallel but make no effort to balance the search across heterogeneous hardware or to prioritize searching high-impact spaces. In this paper, we introduce a framework for massively Scalable Hardware-Aware Distributed Hyperparameter Optimization (SHADHO). Our framework calculates the relative complexity of each search space and monitors performance on the learning task over all trials. These metrics are then used as heuristics to assign hyperparameters to distributed workers based on their hardware. We first demonstrate that our framework achieves double the throughput of a standard distributed hyperparameter optimization framework by optimizing SVM for MNIST using 150 distributed workers. We then conduct model search with SHADHO over the course of one week using 74 GPUs across two compute clusters to optimize U-Net for a cell segmentation task, discovering 515 models that achieve a lower validation loss than standard U-Net.Comment: 10 pages, 6 figure

A I-V analysis of irradiated Gallium Arsenide solar cells

A computer program was used to analyze the illuminated I-V characteristics of four sets of gallium arsenide (GaAs) solar cells irradiated with 1-MeV electrons and 10-MeV protons. It was concluded that junction regions (J sub r) dominate nearly all GaAs cells tested, except for irradiated Mitsubishi cells, which appear to have a different doping profile. Irradiation maintains or increases the dominance by J sub r. Proton irradiation increases J sub r more than does electron irradiation. The U.S. cells were optimized for beginning of life (BOL) and the Japanese for end of life (EOL). I-V analysis indicates ways of improving both the BOL and EOL performance of GaAs solar cells