Practicum anxiety and English language preservice teacher preparation in Indonesia: Experiences from the COVID-19 pandemic (preprint)

The existing literature on practicum anxiety seldom scrutinises the preservice teachers’ experiences to uncover potential links between the sources of these anxieties and the conceptual frameworks they bring into their practicum journeys. This qualitative study interviewed five first-year preservice English language teachers in Indonesia amid COVID-19 lockdowns, exploring sources of their anxiety and coping strategies. The analysis revealed that, while their coping strategies align with those documented in prior international research, it also yielded novel insights. The preservice teachers in the study lacked conceptual frameworks to aid in translating the national ELT curriculum objectives into practice, did not mention utilising relevant professional literature for additional guidance, and appeared unfamiliar with the school culture at their practicum locations. Arguably, these factors are crucial for fostering a sense of ease during the practicum. Although the scope of this study is limited, its findings highlight the significance of this analysis and stress the necessity for more extensive investigations to gain a fuller understanding of the preservice practicum experience.

Telepractice in the diagnosis and treatment of pediatric speech-language disorders: The opinions and experiences of speech-language pathologists.

PURPOSE: This prospective cross-sectional study aimed to investigate the opinions and experiences with telepractice (TP) of Dutch-speaking speech-language pathologists (SLPs) living in the Dutch-speaking part of Belgium (Flanders). This study will help to optimize care for children with speech-language disorders as we will gain more insight into the experienced barriers and facilitators while using TP for assessing and treating these disorders. METHOD: Twenty-nine Dutch-speaking speech-language pathologists living in Flanders (age category 20-30 years: n = 16/29, 55.2%, 31-40 years: n = 10/29, 34.2%, 41-50 years: n = 2/29, 6.9%, 51-60 years: n = 1/29, 3.4%) were recruited through the social media. An online questionnaire was developed based on the available literature and administered to the SLPs. To compare the opinions and experiences of SLPs with TP, χ2 tests or Fisher's exact tests were used. RESULTS: The study showed a statistically significant association between years of clinical experience of SLPs and their opinion that TP does not provide more options in a clinical setting compared to face-to-face contact. SLPs who had expertise in multiple domains experienced significantly more added value of TP during the corona pandemic than SLPs who had expertise in only one specific domain. Additionally, SLPs who worked in a private practice indicated significantly more difficulties in developing a therapeutic relationship due to a lack of personal contact than SLPs who worked in other settings. 51.7% (15/29) of the SLPs experienced technical barriers using TP. CONCLUSION: Expertise in multiple domains of pediatric speech-language therapy resulted in experiencing more added value of TP during the corona pandemic, possibly because of the experience of multiple different and simultaneous advantages of TP in several domains. Additionally, SLPs in a private practice experienced more difficulties in developing a therapeutic relationship due to a lack of personal contact with their clients. This is in contrast to hospitals where children are often seen for a shorter period. Hence, there may be less chance of a negative perception of relationships with clients. Another conclusion is that treatment drop-out was not larger using TP compared to face-to-face therapy. However, SLPs experienced that the use of TP was not promoted/encouraged by their employer possibly because of technical barriers. It is hoped that the findings of this study will help SLPs and policymakers overthrow existing barriers and make telepractice a substantial, effective, and efficient service delivery model.

Leveraging Social Interactions to Detect Misinformation on Social Media (preprint)

Detecting misinformation threads is crucial to guarantee a healthy environment on social media. We address the problem using the data set created during the COVID-19 pandemic. It contains cascades of tweets discussing information weakly labeled as reliable or unreliable, based on a previous evaluation of the information source. The models identifying unreliable threads usually rely on textual features. But reliability is not just what is said, but by whom and to whom. We additionally leverage on network information. Following the homophily principle, we hypothesize that users who interact are generally interested in similar topics and spreading similar kind of news, which in turn is generally reliable or not. We test several methods to learn representations of the social interactions within the cascades, combining them with deep neural language models in a Multi-Input (MI) framework. Keeping track of the sequence of the interactions during the time, we improve over previous state-of-the-art models.

A Large Language Modelling Deep Learning Framework for the Next Pandemic (preprint)

Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Typical applications include 1) medical report generation, 2) disease classification, and 3) survival prediction. Since most neural networks are supervised, their quality heavily depends on the volume and quality of available labels. However, for novel diseases, e.g., new pandemics or new variants, there are few existing labels. In addition, the acquisition of new pandemic cases to collect sufficient labels for training is time-consuming and is typically unavailable at the early stage. To prepare neural networks for the next pandemic, in this paper, we propose a large language model - Unsupervised Learning from Unlabelled Medical Images and Text (ULUMIT) framework, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering new pandemics, our framework can be rapidly deployed and easily adapted to them with extremely limited labels. Furthermore, ULUMIT supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for any clinical task that involves both visual and textual medical data. We demonstrate the effectiveness of our ULUMIT by showing how it would perform using the COVID-19 pandemic ``in replay''. In particular, in the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input medical data, image-only, text-only, and image-text; 2) three kinds of downstream tasks, medical reporting, diagnosis, and prognosis; 3) five public COVID-19 datasets; and 4) three different languages, i.e., English, Chinese, and Spanish. All experiments consistently show that our framework can make accurate and robust COVID-19 decision-support tasks with little labelled data (such as considering information from only one patient), providing an impact on medical data analysis during the early stage of the next pandemic. Besides COVID-19, our framework can be applied to identify 14 common thorax diseases and tuberculosis across five additional public datasets, demonstrating its robustness in generalization and transferability. In brief, our framework achieves state-of-the-art performances on ten datasets.

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature: a retrospective study (preprint)

Background: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19 related publications to help scale-up the epidemiological curation process. Methods: In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6,365 publications manually classified into two classes, three subclasses and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results: The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion: This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.

Functional and microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19 (preprint)

Background: Little is known about the underpinning mechanisms of neurological dysfunction in post-COVID syndrome. Methods: We conducted a cross-sectional study of 87 consecutive subjects after a mild infection, with a median of 54 days after diagnosis of COVID-19. We performed structured interviews, neurological examinations, 3T-MRI scans, and neuropsychological assessments. The MRI study included white matter investigation with diffusion tensor images (DTI) and functional connectivity with resting-state functional MRI (RS-fMRI). Results: Subjects self-reported headaches (40%) and memory difficulties (33%). The quantitative analyses confirmed symptoms of fatigue (68% of participants), excessive somnolence (35%), symptoms of anxiety (29%), impaired cognitive flexibility (40%), and language dysfunction (33%). Besides, we observed a correlation between DTI fractional anisotropy (FA) and abnormal attention and cognitive flexibility in the Trail Making Test part B. Elevated levels of fatigue and somnolence associated with higher connectivity of the posterior cingulate cortex (PCC) in the RS-fMRI study of the default mode network. While higher connectivity of the PCC with bilateral angular gyri was associated with higher fatigue levels, the elevated levels of somnolence correlated with higher connectivity between the PCC and both the left thalamus and putamen. Conclusions: COVID-19 is associated with long-term neuropsychiatric symptoms and cerebral functional and microstructural alterations.

Metacognitive Strategies, Language Learning Motivation, Self-Efficacy Belief, and English Achievement During Remote Learning: A Structural Equation Modelling Approach (preprint)

Metacognitive strategies, language learning motivation, and self-efficacy belief are crucial to online or remote learning success. The purpose of the present study was to evaluate the interrelationship among metacognitive strategies, language learning motivation, self-efficacy belief, and English learn- ing achievement. The data were collected from three surveys and an English test. The participants were 590 Chinese university students. The findings revealed that self-efficacy belief predicts English learning achievement. In particular, metacognitive strategies and language learning motiva- tion mediate the predictive effects of self-efficacy belief on English learning achievement. The find- ings show the potential of enhancing online English learning achievement by facilitating learners’ self-efficacy belief, motivation, and metacognitive strategies. Implications can be gained for remote learning within and beyond the coronavirus (COVID-19) context.

Telemedicine in primary care of older adults: Results of a multi-phase study (preprint)

Background The COVID-19 pandemic transformed the health care systems, motivating Telemedicine's rapid evolution and implementation. Telemedicine (TM) can potentially improve the quality of primary health care and increase accessibility to the population. Nevertheless, its use may represent a challenge to older people as they may have different needs from the general population due to potential age-related changes in perceptual, motor and cognitive capacities. We thus aimed to identify potential facilitators and barriers to Telemedicine (TM) use in the primary care of older adults and develop recommendations.Methods We conducted a multi-phase study: 1. A systematic mixed-method review to explore determinants in the use of TM for older adults for papers published before July 2021; 2. Qualitative descriptive study, we interviewed 29 older adults and conducted three focus groups and one deliberative dialogue with healthcare professionals from four McGill family medicine sites. The findings were analyzed using deductive thematic analysis based on the Consolidated Framework for Implementation Research (CFIR); 3. We integrated the results from both phases and the deliberative dialogue using thematic analysis.Results The systematic review identified over 3,328 references. We included 21 articles, resulting in positive experiences and high satisfaction and generating interest in TM as a complementary healthcare delivery model. Participants agreed that TM contributed to maintaining the continuity of care and was convenient when there is a previous/established patient-physician relationship and to resolve minor health issues. TM was beneficial for persons with limited mobility; and reduced the exposure of older adults to potential high-risk environments. Nevertheless, participants expressed concerns about the lack of visual contact, causing essential details to be overlooked. Similarly, miscommunication difficulties may emerge due to language or hearing barriers. Family physicians perceived that most patients did not consider phone consultations a medical act. However, participants were amenable to a hybrid approach, combining in-person consultations and Telemedicine, depending on their health conditions.Conclusions Older adults and healthcare professionals consider Telemedicine a good alternative for accessing healthcare services, though it would be necessary to promote a hybrid approach and encourage and support familiarization, adaptability, and accessibility to technological tools.

Hemiplegic Migraine as Cause For Motor Vehicle Collision: A Case Report (preprint)

Introduction: Hemiplegic migraine (HM) is a rare, heterogenous form of migraine characterized by unilateral weakness. This motor aura can present with reversible visual, sensory, and language deficits. HM can be difficult to diagnose due to overlapping presentation with other complex conditions such as multiple sclerosis, seizure disorders, and transient ischemic attack (TIA). Case Presentation: We describe a case of a 40 year old with asymptomatic COVID-19 infection who presented after a motor vehicle collision caused by HM consistent with left sided weakness and loss of consciousness. Conclusions: To date, this is the first description of a patient with known complex migraines to have a motor vehicle collision as a result of HM. The risk of HM-associated neurologic symptoms while driving poses a significant public safety concern. We suggest driving restrictions be considered in patients with HM when migraine aura is present. This case presents support to examine active infection with SARS-CoV-2 as a trigger for HM.

The psychological burden associated with Ehlers-Danlos syndromes: a systematic review.

CONTEXT: Ehlers-Danlos syndromes (EDS) are disorders of connective tissue that lead to a wide range of clinical presentations. While we are beginning to understand the association between EDS and psychological manifestations, it is critical that we further elucidate the relationship between the two. Understanding the correlation between EDS and mental health will better ensure swift diagnosis and effective treatment for patients. OBJECTIVES: This study aims to systematically examine and report the prevalence of psychiatric disorders in the EDS population. METHODS: The PubMed database was searched on June 14, 2021 for articles published from January 2011 to June 2021. We included original, evidence-based, peer-reviewed journal articles in English that reported information on psychiatric disorders among EDS patients. Psychiatric disorders and psychological conditions were limited to those included in the "psychology" and "mental disorders" Medical Subject Headings (MeSH) search terms defined by the National Library of Medicine. Publications identified utilizing this search strategy by M.K. were imported into the Covidence system, where they first underwent a title and abstract screening process by three independent reviewers (M.K., K.L., H.G.). During the full-text review, two independent reviewers read the full text of the questionable articles to assess their eligibility for inclusion. Studies were excluded if they did not meet our target objective or if they were not in English or if they were opinion pieces, conference abstracts, or review articles. Data were extracted from the shortlisted studies by reviewers. During the data extraction phase, the quality and risk of publication bias were assessed by two independent reviewers utilizing the National Institutes of Health (NIH) Study Quality Assessment Tools. Any disagreements in study selection, data extraction, or quality assessment were adjudicated via discussion between the two reviewers, utilizing a third reviewer as a decider if necessary. RESULTS: Out of 73 articles identified, there were no duplicates. A total of 73 records were screened, but only 40 articles were assessed in full text for eligibility. A total of 23 articles were ultimately included, which collectively discussed 12,298 participants. Ten (43.5%) of the included studies were cross-sectional in design, three (13.0%) were case reports, and three (13.0%) were retrospective chart reviews. The remaining seven (30.4%) articles were either case-control, cohort, qualitative, controlled observational, or validation studies. Twelve (52.2%) of the studies reported data on depression disorders, six of which reported prevalence data. Nine (39.1%) of the studies reported data on anxiety disorders, five of which reported prevalence data. Studies that reported nonprevalence data presented odds-ratio, mean scores on psychiatric evaluations, and other correlation statistics. Psychiatric disorders that were most reported in these articles were mood disorders (n=11), anxiety disorders (n=9), and neurodevelopmental disorders (n=7). Although the reports varied, the highest psychiatric prevalence reports in EDS patients involved language disorders (63.2%), attention-deficit/hyperactivity disorder (ADHD) (52.4%), anxiety (51.2%), learning disabilities (42.4%), and depression (30.2%). CONCLUSIONS: Although mood disorders were cited in more articles, the highest reported prevalence was for language disorders and ADHD. This discrepancy highlights the importance of performing more research to better understand the relationship between EDS and psychiatric disorders.

Language Models for the Prediction of SARS-CoV-2 Inhibitors (preprint)

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ~9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Functional and microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19 (preprint)

Although post-acute cognitive dysfunction and neuroimaging abnormalities have been reported after hospital discharge in patients recovered from COVID-19, little is known about persistent, long-term alterations in people without hospitalization. We conducted a cross-sectional study of 87 non-hospitalized recovered individuals 54 days after the laboratory confirmation of COVID-19. We performed structured interviews, neurological examination, 3T-MRI scans with diffusion tensor images (DTI) and functional resting-state images (fMRI). Also, we investigated fatigue, anxiety, depression, somnolence, language, memory, and cognitive flexibility, using validated instruments. Individuals self-reported a high frequency of headache (40%) and memory difficulties (33%). The quantitative analyses confirmed symptoms of fatigue (68%), excessive somnolence (35%), anxiety (29%), impaired cognitive flexibility (40%) and language impairment (33%). There were widespread cerebral white matter alterations (mainly characterized by increased fractional anisotropy), which correlated with abnormal attention and cognitive flexibility. The resting-state fMRI networks analysis showed severely disrupted brain hyperconnectivity and loss of resting-state networks specificity.

One Year of Evidence on Mental Health in the COVID-19 Crisis - A Systematic Review and Meta-Analysis (preprint)

ABSTRACT Objective: This paper provides a systematic review and meta-analysis on the prevalence rate of mental health issues of the major population, including general population, general healthcare workers (HCWs), and frontline healthcare workers (HCWs), in China over one year of the COVID-19 crisis. Design: A systematic review and meta-analysis. Data sources: articles in PubMed, Embase, Web of Science, and medRxiv up to November 16, 2020, one year after the first publicly known confirmed COVID-19 case. Eligibility criteria and data analysis: any COVID-19 and mental disorders relevant English studies with frontline/general healthcare workers, general adult population sample, using validated scales. We pooled data using random-effects meta-analyses to estimate the prevalence rates of anxiety, depression, distress, general psychological symptoms (GPS), insomnia, and PTSD and ran meta-regression to tease out the heterogeneity. Results: The meta-analysis includes 131 studies and 171 independent samples. The overall prevalence of anxiety, depression, distress, GPS, insomnia, and PTSD are 11%, 13%, 20%, 13%, 19%, and 20%, respectively. The meta-regression results uncovered several predictors of the prevalence rates, including severity (e.g., above severe vs. above moderate, p<0.01; above moderate vs. above mild, p<0.01) and type of mental issues (e.g., depression vs. anxiety, p=0.04; insomnia vs. anxiety p=0.04), population (frontline HCWs vs. general HCWs, p<0.01), sampling location (Wuhan vs. non-Wuhan, p=0.04), and study quality (p=0.04). Limitations: First, we only focus on China population, which may limit the generalizability of the results. Second, 96.2% studies included in this meta-analysis were cross-sectional. Last, since we only included studies published in English, we expect to have a language bias. Conclusion: Our pooled prevalence rates are significantly different from, yet largely between, the findings of previous meta-analyses, suggesting the results of our larger study are consistent with, yet fine-tune, the findings of the smaller, previous meta-analyses. Hence, this meta-analysis not only provides a significant update on the mental health prevalence rates in COVID-19 but also suggests the need to update meta-analyses continuously to provide more accurate estimates of the prevalence of mental illness during this ongoing health crisis. While prior meta-analyses focused on the prevalence rates of mental health disorders based on one level of severity (i.e., above mild), our findings also suggest a need to examine the prevalence rates at varying levels of severity. The one-year cumulative evidence on sampling locations (Wuhan vs. non-Wuhan) corroborates the typhoon eye effect theory. Our finding that the prevalence rates of distress and insomnia and those of frontline healthcare workers are higher suggest future research and interventions should pay more attention to those mental outcomes and populations. Keywords: systematic review; meta-analysis; COVID-19; mental health; epidemic; general population; healthcare workers; frontline healthcare workers

Taking Language Samples Home: Feasibility, Reliability, and Validity of Child Language Samples Conducted Remotely With Video Chat Versus In-Person.

Purpose There has been increased interest in using telepractice for involving more diverse children in research and clinical services, as well as when in-person assessment is challenging, such as during COVID-19. Little is known, however, about the feasibility, reliability, and validity of language samples when conducted via telepractice. Method Child language samples from parent-child play were recorded either in person in the laboratory or via video chat at home, using parents' preferred commercially available software on their own device. Samples were transcribed and analyzed using Systematic Analysis of Language Transcripts software. Analyses compared measures between-subjects for 46 dyads who completed video chat language samples versus 16 who completed in-person samples; within-subjects analyses were conducted for a subset of 13 dyads who completed both types. Groups did not differ significantly on child age, sex, or socioeconomic status. Results The number of usable samples and percent of utterances with intelligible audio signal did not differ significantly for in-person versus video chat language samples. Child speech and language characteristics (including mean length of utterance, type-token ratio, number of different words, grammatical errors/omissions, and child speech intelligibility) did not differ significantly between in-person and video chat methods. This was the case for between-group analyses and within-child comparisons. Furthermore, transcription reliability (conducted on a subset of samples) was high and did not differ between in-person and video chat methods. Conclusions This study demonstrates that child language samples collected via video chat are largely comparable to in-person samples in terms of key speech and language measures. Best practices for maximizing data quality for using video chat language samples are provided.

An investigation of Modern Foreign Language (MFL) teachers and their cognitions of Computer Assisted Language Learning (CALL) amid the COVID-19 health pandemic (preprint)

A study was performed with 33 Modern Foreign Language (MFL) teachers to afford insight into how classroom practitioners interact with Computer Assisted Language Learning (CALL) in Second Language (L2) pedagogy. A questionnaire with CALL specific statements was completed by MFL teachers who were recruited via UK based Facebook groups. Significantly, participants acknowledged a gap in practice from the expectation of CALL in the MFL classroom. Overall, respondents were shown to be interested and regular consumers of CALL who perceived its ease and importance in L2 teaching and learning.

[Multifocal encephalitis as a neurological manifestation of COVID-19 infection]. / Encefalitis multifocal como manifestación neurológica de la infección por COVID-19.

TITLE: Encefalitis multifocal como manifestación neurológica de la infección por COVID-19.

Provision of speech-language pathology services for the treatment of speech and language disorders in children during the COVID-19 pandemic: Problems, concerns, and solutions.

Recently, a novel virus has spread worldwide causing the disease called COVID-19. In addition to putting people's lives at risk and causing mortality, various problems have occurred due to the negative effects of the COVID-19 pandemic. Quarantine, social distancing, and the obligation to use protective tools have led to sometimes long term closing of various jobs and services, including rehabilitation services. For instance, the disease has interrupted the provision of Speech-Language Pathology (SLP) services to children due to the need for face-to-face communication between Speech and Language Pathologists (SLPs) and children during the evaluation and treatment processes. Therefore, here, we described the quality of providing SLP services during the COVID-19 pandemic and the negative effects of the disease on the provision of SLP services. In addition, we made an attempt to explain concerns and problems raised by the families, the importance of providing SLP services during the critical period of speech and language development, telepractice services, the roles of speech-language-hearing related scientific associations, and the roles of SLPs during the outbreak of COVID-19.