Gender Differences in the Relationships between Research Impact and Compensation and Promotion: A Case Study Among PhD/PharmD

We examine whether the effects of research impact on faculty compensation and promotion to full professor differ for male and female associate and full professors in the Faculty of Medicine & Dentistry at the University of Alberta. We exclude faculty with MDs and DDSs and proxy for research impact using the faculty member’s h-index, where h represents the number of publications that have been cited at least h times. We find that while the compensation of male faculty members increases by 0.6% for every one-unit increase in the h-index, the compensation of female faculty is essentially uncorrelated with their h-indices. We likewise find that for female faculty to be promoted to full professor they have to have higher research impact proxies than their male peers. Our findings highlight the urgent need for more research on the gendered relationships between research impact and career rewards among faculty.Nous avons évalué l’impact de la recherche sur la rémunération du corps professoral et la promotion au rang de professeur titulaire, entre les professeurs agrégés et titulaires hommes et femmes de la Faculté de médecine et de dentisterie de l’Université de l’Alberta. Nous avons exclu les professeurs cliniciens ayant un doctorat en médecine (MD) ou un doctorat en médecine dentaire (DDSs). L’impact de la recherche a été évalué à l’aide de l’indice h de chaque membre du corps professoral, oùh représente le nombre de publications qui ont été citées au moins h fois. Nous constatons ainsi que tandis que la rémunération des hommes augmente en moyenne de 0,6 % pour chaque augmentation d’une unité de l’indice h, la rémunération des femmes est essentiellement non corrélée avec leurs indices h. Nous constatons également que les professeures atteignent la parité pour la  promotion au poste de professeur titulaire avec les professeurs masculins équivalents à des indicateursd’impact de la recherche considérablement supérieurs aux valeurs médianes normalement attendues pour une promotion au poste de professeur titulaire. Nos résultats mettent en évidence le besoin urgent de plus de recherches sur les relations entre le genre et l’impact de la recherche sur l’avancement de la carrière chez les professeurs-chercheurs

Exploration des perceptions de résidents sur la mise en œuvre préliminaire de la formation médicale fondée sur les compétences

Background: Competence by design (CBD) is a nationally developed hybrid competency based medical education (CBME) curricular model that focuses on residents’ abilities to promote successful practice and better meet societal needs. CBD is based on a commonly used framework of five core components of CBME: outcome competencies, sequenced progression, tailored learning experiences, competency-focused instruction and programmatic assessment. There is limited literature concerning residents’ perceptions of implementation of CBME. Objective: We explored resident perceptions of this transformation and their views as they relate to the intended framework. Methods: We recruited residents enrolled in current CBME implementation between August 2018 and January 2019. We interviewed residents representing eight disciplines from the initial two CBME implementation cohorts. Inductive thematic analysis was used to analyse the data through iterative consensus building until saturation. Results: We identified five themes: 1) Value of feedback for residents; 2) Resident strategies for successful Entrustable Professional Activity observation completion; 3) Residents experience challenges; 4) Resident concerns regarding CBME; and 5) Resident recommendations to improve existing challenges. We found that while there was clear alignment with residents’ perceptions of the programmatic assessment core CBME component, alignment was not as clear for other components. Conclusions: Residents perceived aspects of this transformation as helpful but overall had mixed perceptions and variable understanding of the intended underlying framework. Understanding and disseminating successes and challenges from the resident lens may assist programs at different stages of CBME implementation.Contexte : La « Compétence par conception » (CPC) est un modèle hybride pour les cursus formation médicale fondée sur les compétences (FMFC) développé à l’échelle nationale, qui met l’accent sur les capacités des résidents à promouvoir une pratique médicale réussie et à mieux répondre aux besoins de la société. La CPC repose sur un cadre couramment utilisé de cinq composantes essentielles de la FMFC : les compétences en matière de résultats, la progression séquentielle, les expériences d’apprentissage sur mesure, l’enseignement axé sur les compétences et l’évaluation programmatique. Il y a peu d’études sur les perceptions des résidents quant à la mise en oeuvre de la FMFC. Objectif : Nous avons recueilli les perceptions des résidents en lien avec cette transformation du cursus ainsi que leur point de vue sur le cadre prévu de celui-ci. Méthodes : Nous avons recruté des résidents qui étaient inscrits dans un programme en cours de mise en œuvre de la FMFC entre août 2018 et janvier 2019. Les résidents interrogés des deux premières cohortes de mise en œuvre de la FMFC représentaient huit disciplines. Les données ont fait l’objet d’une analyse thématique inductive par la recherche itérative d’un consensus jusqu’à saturation. Résultats : Nous avons identifié cinq thèmes : 1) la valeur de la rétroaction pour les résidents; 2) les stratégies des résidents pour la réussite des activités professionnelles confiables; 3) difficultés éprouvées par les résidents; 4) les préoccupations des résidents concernant la FMFC; et 5) les recommandations des résidents pour palier aux difficultés existantes. Nous avons constaté que s’il y avait une correspondance claire entre les perceptions des résidents et la composante centrale de la FMFC qu’est l’évaluation programmatique, la correspondance n’était pas aussi évidente pour les autres composantes. Conclusions : Les résidents ont perçu certains aspects du nouveau modèle comme étant utiles, mais dans l’ensemble, leurs perceptions étaient mitigées et leur compréhension de son cadre sous-jacent était variable. La compréhension et le partage de la vision des résidents quant aux succès et défis du modèle peuvent être utiles aux programmes à diverses étapes de la mise en œuvre de la FMFC

The Red Fox (Vulpes vulpes) as Sentinel for Tick-Borne Encephalitis Virus in Endemic and Non-Endemic Areas

Tick-borne encephalitis (TBE) is one of the most important viral zoonosis caused by a neurotropic arbovirus (TBEV). In Germany, TBE is classified as a notifiable disease with an average of 350 autochthonous human cases annually. The incidence-based risk assessment in Germany came under criticism because every year, a number of autochthonous human TBE cases have been detected outside of the official risk areas. Therefore, it is necessary to find additional parameters to strengthen TBEV surveillance. The aim of this study was to examine red foxes as sentinels for TBE. Thus far, there are no published data about the sensitivity and specificity for serological methods testing fox samples. Hence, we aimed to define a system for the screening of TBEV-specific antibodies in red foxes. A total of 1233 fox sera were collected and examined by ELISA and IIFA and confirmed by micro-NT. The overall seroprevalence of antibodies against TBEV in red foxes from Germany confirmed by micro-NT was 21.1%. The seroprevalence differed significantly between risk (30.5%) and non-risk areas (13.1%), with good correlations to local TBE incidence in humans. In conclusion, serological monitoring of red foxes represents a promising surrogate marker system and may even determine unexpected TBEV foci in regions currently regarded as non-risk areas

Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer.

Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved via deep learning-based automated cell segmentation with a 2D U-Net convolutional neural network. The model was trained and tested using images of pathologically diverse human thyroid cells and evaluated by comparing the number of cells selected, segmented areas, and Fpol values obtained using automated (AU) and manual (MA) data processing methods. Overall, the model segmented 15.8% more cells than the human operator. Differences in AU and MA segmented cell areas varied between - 55.2 and + 31.0%, whereas differences in Fpol values varied from - 20.7 and + 10.7%. No statistically significant differences between AU and MA derived Fpol data were observed. The largest differences in Fpol values correlated with greatest discrepancies in AU versus MA segmented cell areas. Time required for auto-processing was reduced to 10 s versus one hour required for MA data processing. Implementation of the automated cell analysis makes quantitative fluorescence polarization-based diagnosis clinically feasible

Applicability of Immersive Analytics in Mixed Reality: Usability Study

Nowadays, visual analytics is mainly performed by programming approaches and viewing the results on a desktop monitor. However, due to the capabilities of smart glasses, new user interactions and representation possibilities become possible. This refers especially to 3D visualizations in the medical field, as well as, the industry domain, as valuable depth information can be related to the complex real-world structures and related data, which is also denoted as immersive analytics. However, the applicability of immersive analytics and its drawbacks, especially in the context of mixed reality, are quite unexplored. In order to validate the feasibility of immersive analytics for the aforementioned purposes, we designed and conducted a usability study with 60 participants. More specifically, we evaluated the effects of spatial sounds, performance changes from one analytics task to another, expert status, and compared an immersive analytics approach (i.e., a mixed-reality application) with a desktop-based solution. Participants had to solve several data analytics tasks (outlier’s detection and cluster recognition) with the developed mixed-reality application. Thereby, the performance measures regarding time, errors, and movement patterns were evaluated. The separation into groups (low performer vs. high performer) was performed using a mental rotation pretest. When solving analytic tasks in mixed reality, participants changed their movement patterns in the mixed reality setting significantly, while the use of spatial sounds reduced the handling time significantly, but did not affect the movement patterns. Furthermore, the usage of mixed reality for cluster recognition is significantly faster than the desktop-based solution (i.e., a 2D approach). Moreover, the results obtained with self-developed questionnaires indicate 1) that wearing smart glasses are perceived as a potential stressor and 2) that the utilization of sounds is perceived very differently by the participants. Altogether, industry and researchers should consider immersive analytics as a suitable alternative compared to the traditional approaches