Exploring the Reactions of Peer Learners to a New Model of Peer-Assisted Simulation-Based Learning Clinical Placement

In response to the paucity of clinical placements available in 2020 due to the COVID-19 pandemic, alternate options for prelicensure students were necessary in order for them to complete the fieldwork required for graduation. In response, Curtin University replaced a faculty-led fully-simulated placement with a peer-assisted learning model. This incorporated final-year students acting as peer teachers to penultimate-year students, thus creating new learning and teaching placements for the final-year students. To our knowledge, this had never been done on such a scale before. Considering the importance of meeting learner expectations in the tertiary setting, the perceptions of peer learners around the innovation were important but unknown. This study used a prospective qualitative observational design that utilized feedback from peer learners relating to learning using the peer-assisted model. Peer learners provided written reflections that were analysed thematically. During November and December 2020, 171 penultimate-year physiotherapy students participated in one of two three-week placements, and 170 consented to participate in data collection. Qualitative data reflected several enablers and barriers to learning during the experience. These related to the peer teacher attributes, the provision of performance feedback, the learning environment, and the facilitation of clinical reasoning. Peer learners enjoyed the peer-assisted model, found peer teachers able to facilitate learning, and provided useful insights that will shape future placements. The success of the model supports repeating it in the future. This will maintain a bilateral exchange of peer-led clinical learning and teaching with diminished faculty supervisory workload.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

BACKGROUND: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. METHODS: This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. INSTITUTION: A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. RESULTS: Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). CONCLUSION: The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

Sentiment Classification Against the Public Activity Restrictions Policy in Jakarta Using Machine Learning Models

To stop the spread of the COVID-19, the Indonesian government implemented community activities restrictions enforcement (in Indonesian language: Pemberlakuan Pembatasan Kegiatan Masyarakat or PPKM) starting from January 2021. The term PPKM applied are PPKM Mikro (in Indonesian language) or Micro PPKM, PPKM Darurat (in Indonesian language) or Emergency PPKM, and PPKM Level 1-4 or Level 1-4 PPKM. On the other hand, the existing research mostly used Twitter as the data source to do sentiment classification. Therefore, we aimed to classify social media comments on Facebook and YouTube on Level 1-4 PPKM policy in Jakarta. We used "PPKM Jakarta"as the keyword topic in August - September 2021 when Level 1-4 PPKM was ongoing. In addition, we compared datasets composition, machine learning models, and features extraction. Random Forest, Naive Bayes, and Logistic Regression were performed as the machine learning models due to they were the top three models on the previous research. We extracted word unigram, word bigram, character trigram, and character quadrigram as the feature extraction. The highest average F-measure was obtained with a 79.6% score of the Logistic Regression model using character quadrigram extraction. We found that comments from Facebook and YouTube were dominated by neutral sentiment (49.8%) with this setup. It means the people of Jakarta started to trust the government in handling the COVID-19 pandemic. Through word cloud analysis, it is recommended that social assistance be reviewed for those directly affected. © 2021 IEEE.

Ace2 and Tmprss2 Expressions Are Regulated by Dhx32 and Influence the Gastrointestinal Symptoms Caused by SARS-CoV-2.

Studies showed that the gastrointestinal (GI) tract is one of the most important pathways for SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19). As SARS-CoV-2 cellular entry depends on the ACE2 receptor and TMPRSS2 priming of the spike protein, it is important to understand the molecular mechanisms through which these two proteins and their cognate transcripts interact and influence the pathogenesis of COVID-19. In this study, we quantified the expression, associations, genetic modulators, and molecular pathways for Tmprss2 and Ace2 mRNA expressions in GI tissues using a systems genetics approach and the expanded family of highly diverse BXD mouse strains. The results showed that both Tmprss2 and Ace2 are highly expressed in GI tissues with significant covariation. We identified a significant expression quantitative trait locus on chromosome 7 that controls the expression of both Tmprss2 and Ace2. Dhx32 was found to be the strongest candidate in this interval. Co-expression network analysis demonstrated that both Tmprss2 and Ace2 were located at the same module that is significantly associated with other GI-related traits. Protein-protein interaction analysis indicated that hub genes in this module are linked to circadian rhythms. Collectively, our data suggested that genes with circadian rhythms of expression may have an impact on COVID-19 disease, with implications related to the timing and treatment of COVID-19.

Towards Evaluating Students' Digital Capabilities: An Analysis of UK Further Education Student Surveys

The ability of students to access learning via technology is a key factor in sustainable development goals. During the COVID-19 emergency most students' educational experience moved from face-To-face physical classroom to web-based environments which exposed disparities in students' digital resources and competence and placed greater attention on the need to address these inequalities. Digital competence is typically measured in terms of an individual's ability to use digital technology to achieve their work, study or personal objectives. Being digitally competent is significant with regard to an individual being able to achieve things that they value, but is only part of an overall evaluation of their digital capability. This paper argues that in addition to competence, assessment of digital capability should include an evaluation of a person's access to technology as well as their attitudes towards its value in achieving their goals. This paper is a work in progress exploring findings derived from research evaluating strategies to improve staff capability and confidence in using online learning technologies at eight FE colleges in the south east of England. In this research students undertook surveys that included self-Assessment of their digital competences following the DigComp model, information on their use of digital devices and home network reliability, and evaluated their enjoyment of and confidence in using online learning technologies. This current paper explores the outcomes from these surveys. Evaluation of survey data revealed a significant digital divide between those who had access to suitable devices and reliable network connections and those who did not. Results show significant associations between students' access to the technology they need to take part in online lessons, their self-Assessed competence, and their capability to fully engage with and satisfaction with online learning. This paper suggests that these factors should be considered as part of a ĝ€digital capabilities index' when undertaking evaluations of individual student needs and identifying potential ĝ€at risk' students. © 2021 ACM.

Infrastructure Matters: A New Model for Strengthening Public Health

To help policy-makers and public health leaders more effectively make their case for increased funding, we proposed a new conceptual model that describes in simple terms how investments in infrastructure, using our knowledge of essential functions and foundational public health services, creates the building blocks upon which individual public health programs can succeed in protecting and promoting the public's health while preventing disease [4]. Following the publication of the Institute of Medicine's report The Future of Public Health [6], a serious decades-long stepwise approach involving local, state, federal, tribal, territorial, academic, and philanthropic participation has clearly defined public health's core functions and essential services, established rigorous standards and metrics, and provided a framework for accrediting public health agencies [7,8]. The World Health Organization has developed standards and measures (International Health Regulations 2005) directed at strengthening national-level public health systems to prevent and control national outbreaks and global pandemics [11].

Dental treatment and oral health professional in the midst of coronavirus disease 2019 (Covid-19) pandemic outbreak

The current spread of novel coronavirus disease 2019 (COVID-19) has brought deep impact to the entire international community and caused worldwide public health concerns. An outbreak of novel coronavirus disease 2019 (COVID-19) in China has brought big influence to every aspect of human life. Medical healthcare and Oral healthcare professionals, especially dental general practitioners and dental specialists, are at higher risk of getting infected due to possible close contact with infected patients. As the severe acute respiratory syndrome novel coronavirus disease 2019 (COVID-19) is transmitted mainly through sneezes, droplets and aerosols, hence there is a high risk of transmission during dental treatment procedures in dental hospital or dental clinic. Since the transmission is of high chance during dental procedure, there is a crucial need to increase investigations to detect novel coronavirus disease (COVID-19) in oral fluids/saliva that is important to improve effective strategies for prevention, especially for dental general practitioners, dental specialists and healthcare professionals that perform aerosol-generating procedures. This article describes methods that can be used by both dental and oral healthcare professionals to minimize the risk of cross-contamination in daily routine clinical practice during the current outbreak of novel coronavirus disease 2019 (COVID-19). © 2020 Nova Science Publishers, Inc.

From Helpless to Hero: Promoting Values-Based Behavior and Positive Family Interaction in the Midst of COVID-19.

Parents managing their home environments during government-ordered stay-at-home periods are likely to need new skills for occupying their children's time with activities that promote health and emotional well-being. Moreover, parents and children know they need help managing these circumstances. Perhaps for the first time, behavior analysts hold the reinforcers for increasing parental involvement in effective child-rearing practices. In fact, behavior analysts can help parents enlist their children in managing the household by framing their behavior in terms of hidden superpowers. In the current article, we argue that behavior analysts have a range of tools to offer that are grounded in evidence-based principles, strategies, and kernels-or essential units of behavioral influence. When combined into scheduled daily practices and invoked by children taught to see their use of the tools as nothing short of heroic, these practices function as "vaccinations" that inoculate families against toxic and unsafe behaviors.