Narrowing awarding gaps: the contributory role of policy and assessment type

Awarding gaps between various groups of students persist across the Higher Education sector, yet the responses designed to address the contributors remain localised. The sudden spread of COVID-19 led to various responses across the University sector creating an unprecedented natural experiment and offering the opportunity to compare outcomes from these measures with prior cohorts. This study seeks to investigate the effects of two COVID-19 interventions on students' performance in the Business and Management discipline at a UK university. The specific COVID-19 measures considered here are the move to online assessments and the new grade policy to ensure the pandemic did not affect students' outcomes adversely. We use a Kernel Propensity Score and a Quantile Difference in Differences models to estimate the treatment effect of the two COVID interventions on the treated group, namely term two students' performances of the academic year 2019/20. Our results indicate that the effects of both COVID interventions supported the outcomes of international students, thereby narrowing the awarding gap. Findings suggest firstly that institutional policies adopted in crises should seek to address potential adverse effects on student outcomes for the period of disruption, indicating that significant care should be taken in their drafting. The policy, in this case, was found to have achieved its aim. Secondly, the move to new modes of assessment combined with detailed briefings from faculty may have served to uncover aspects of the hidden curriculum for this group, contributing to a narrowing of awarding gaps between different groups of students.

A Laboratory Validation of Self-Collected Nasal Swab and Rhinoswab for the Detection of Sars-Cov-2

Intro: In Australia, the main methods to diagnose COVID-19 are through rapid antigen tests (RATs) and through nucleic acid amplification testing (NAAT, including polymerase chain reaction) on healthcare worker (HCW)-collected combined nose/throat swabs. With self-collection widely used by the public for RATs, the aim of this study was to evaluate the performance of self-collected samples using commercial NAAT for SARS-CoV-2. Method(s): Consenting participants aged 14 years and older were provided with a self-collection pack containing instructions and either a FLOQSwab (Copan) or a Rhinoswab (Rhinomed). Participants collected their own nasal sample unsupervised prior to having a HCW-collected combined nose and throat swab taken for standard of care NAAT. Paired self-collected and HCW samples were tested on the cobas SARS-CoV-2 assay (Roche) and the Aptima SARS-CoV-2 assay (Hologic). Finding(s): We demonstrated comparable sensitivity, specificity, and agreement between self-collected nasal and Rhinoswab samples, compared to HCW- collected samples tested using the cobas SARS-CoV-2 and Aptima SARS-CoV-2 assays. In our study the clinical performance of self-collected specimens was comparable to HCW-collected samples, with both self-collect nasal and Rhinoswab samples resulting in 90-95% sensitivity, and in most cases >95% specificity. Discussion(s): Without the availability of samples for NAAT the ability to perform genomic testing is limited, reducing surveillance and public health investigations. We showed that genomic sequencing from self-collected samples can correctly identify the virus lineage and that the main determination of successful genomic testing is a high viral load rather than collection method. Conclusion(s): These data support self-collection as an accessible method for community testing for COVID-19 and introduces a novel collection device, the Rhinoswab as an alternative to the standard nasal swab. The testing method of self-collection can be expanded from the widely used RATs to NAAT and genomic testing which may inform the management and public health response to the COVID-19 pandemic.Copyright © 2023

Are we all in this together? Alleviating the childcare constraint for women in economic crises

Purpose: The purpose of the paper is to advocates the use of gendered economic policies to stimulate a post-COVID-19 recovery. It alerts on the risk of ignoring the female dimension of the current crisis and of resorting again to austerity programs that, like the ones enacted after the 2008 crisis, would hit women and mothers disproportionally harder than other groups. Design/methodology/approach: The authors use data from the British Household Panel Survey on female participation and account for gendered constraints and enablers missed by mainstream economics. Using a sequential empirical approach, the authors simulate various welfare policy scenarios that address factors, such as childcare costs, personal and social nudges, that could help women back into the labor market in the aftermath of a crisis. Findings: The authors found that incentive-type interventions, such as subsidies, promote female labor market participation more effectively than punishment-austerity type interventions, such as benefits' cuts. Policies oriented to alleviate childcare constraints can be sustainable and effective in encouraging women back to work. Considering factors wider than the standard economic variables when designing labor market policies may provide fruitful returns. Originality/value: The sequential methodology enables to estimate current and counterfactual incomes for each female in the sample and to calculate their prospective financial gains and losses in changing their labor market status quo, from not employed into employed or vice-versa. Welfare policies affect these prospective gains and losses and, by interacting with other factors, such as education, number and age of children and social capital, prompt changes in women's labor market choices and decision. © 2021, Emerald Publishing Limited.

Image quality assessment for closed-loop computer-assisted lung ultrasound

We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models: a quality assessment module that automates predictions of image quality, and a diagnosis assistance module that determines the likelihood-of-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training the quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Using more than 25,000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86% when classifying between sufficient and insufficient quality images by the quality assessment module. For data of sufficient quality - as determined by the quality assessment module - the mean classification accuracy, sensitivity, and specificity in detecting COVID-19-positive cases were 0.95, 0.91, and 0.97, respectively, across five holdout test data sets unseen during the training of any networks within the proposed system. Overall, the integration of the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at pointof- care. © 2021 SPIE.