A critical evaluation of computational mechanisms of binocular disparity processing

The past decades of research in visual neuroscience have generated a large and disparate body of literature on the computation of binocular disparity in the primary visual cortex. Models have been proposed to describe specific phenomena, yet we lack a theoretical framework which is grounded in neurophysiology and also explains the effectiveness of disparity computation. Here, we examine neural circuits that are thought to play an important role in the computation of binocular disparity. Starting with the binocular energy model (Ohzawa et al. 1990), we consider plausible extensions which include suppressive mechanisms from units tuned to different phase disparities (Tanabe et al. 2011), which is formerly theorized to perform false disparity detection (Read & Cumming 2007) as well as coarse-to-fine (Menz & Freeman 2004a,b) and recurrent processing (Samonds et al. 2013). We rigorously cross-examine the consistency of these circuits with neurophysiology data including ocular dominance and binocular modulation (Ohzawa & Freeman 1990), spike-triggered analysis and temporal dynamics of disparity tuning (Tanabe et al. 2011) and attenuation to anti-correlated stimuli (Cumming & Parker 1997; Tanabe et al. 2011). We further evaluate the ability of the resulting computational models to recover depth, both theoretically and experimentally, using a dataset of natural and synthetic images. Overall, we find that a computational model which combines suppressive mechanisms by units with non-zero phase disparity, contrast normalization as well as lateral interaction between units tuned to specific combinations of phase and position disparities, seems consistent with all of the available V1 neurophysiology data and achieves the highest accuracy in real-world depth computation

Development of a Rubric to Evaluate Implementation Quality of Simulation-based Courses: A Consensus Study

A. Background When simulation-based courses fail, there are at least two reasons: the content and structure were inadequate, or the quality of the implementation process was faulty. Evidence-based, stepwise approaches to ensure the development, delivery, and evaluation of simulation-based courses are well-established. However, best practices for implementation of these courses based on implementation science, are not widely known, or applied. The purpose of this study was to employ consensus building methodology to define content for a rubric, Implementation Quality Rubric for Simulation (IQR-SIM), to evaluate the implementation quality of simulation-based courses in health professions education. B. Methods A three-round, modified Delphi process involving international simulation and implementation experts was initiated to gather and converge opinions regarding criteria for evaluating the implementation quality of simulation-based courses. Candidate items for round 1 were developed based on the Adapted Implementation Model for Simulation (AIM-SIM). The items were revised and expanded to include descriptive anchors for evaluation in round 2. The criterion for inclusion was 70% of respondents selecting an importance rating of 4 or 5/5. Round 3 provided refinement and final approval of the content and descriptive anchors. C. Results Thirty-three experts from 9 countries participated. The initial rubric of 32 items was reduced to 18 items after three Delphi rounds, resulting in the IQR-SIM: a three-point rating scale, with non-scored options “Don’t know/can’t assess” and “Not applicable”, and a comment section. D. Conclusion The IQR-SIM is an operational tool that can be used to evaluate the implementation quality of simulation-based courses and aid in the implementation process to identify gaps, monitor the process, and promote the achievement of desired implementation and learning outcomes

Training and education of healthcare workers during viral epidemics: a systematic review

Background It is necessary to train a large number of healthcare workers (HCW) within a limited time to ensure adequate human resources during an epidemic. There remains an urgent need for best practices on development and implementation of training programmes.Objective To explore published literature in relation to training and education for viral epidemics as well as the effect of these interventions to inform training of HCW.Data sources Systematic searches in five databases performed between 1 January 2000 and 24 April 2020 for studies reporting on educational interventions in response to major viral epidemics.Study eligibility criteria All studies on educational interventions developed, implemented and evaluated in response to major global viral outbreaks from 2000 to 2020.Participants Healthcare workers.Interventions Educational or training interventions.Study appraisal and synthesis methods Descriptive information were extracted and synthesised according to content, competency category, educational methodology, educational effects and level of educational outcome. Quality appraisal was performed using a criterion-based checklist.Results A total of 15 676 records were identified and 46 studies were included. Most studies were motivated by the Ebola virus outbreak with doctors and nurses as primary learners. Traditional didactic methods were commonly used to teach theoretical knowledge. Simulation-based training was used mainly for training of technical skills, such as donning and doffing of personal protective equipment. Evaluation of the interventions consisted mostly of surveys on learner satisfaction and confidence or tests of knowledge and skills. Only three studies investigated transfer to the clinical setting or effect on patient outcomes.Conclusions and implications of findings The included studies describe important educational experiences from past epidemics with a variety of educational content, design and modes of delivery. High-level educational evidence is limited. Evidence-based and standardised training programmes that are easily adapted locally are recommended in preparation for future outbreaks