9 research outputs found

    Posing for the Camera: An Analysis of Pre-service Teachers’ Discursive Practices During a Video Analysis Session

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    In this study, we inquired into the potential that the collective analysis of teaching via a Video Analysis Session (VAS) might serve as a form of critical reflection for pre-service teachers (PSTs) to not only pose questions about their own practice, but to take on a critical stance, or pose, toward their craft. Specifically, we analyzed the experiences of pre-service English Language Arts teachers as they planned for, participated in, and collectively reflected on video clips of their teaching in a collective VAS. We drew on positioning theory and the tools of Conversation Analysis to examine the nature of the interactions among PSTs as they provided feedback to one another during the VAS. Implications suggest that to facilitate critical reflection via a VAS, teacher educators must guide PSTs to consider the myriad contextual factors that shape student learning and teachers’ pedagogical choices

    Making Room for Zoom in Focus Group Methods: Opportunities and Challenges for Novice Researchers (During and Beyond COVID-19)

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    Als die COVID-19-Pandemie über die Welt hereinbrach, waren viele Menschen gezwungen, sich auf online-basierte Routinen einzustellen, darunter auch qualitative Forscher*innen, die nach alternativen Möglichkeiten zur Erhebung aussagekräftiger Daten suchten. Während Fokusgruppen traditionell Face to Face durchgeführt werden, bieten Fortschritte bei Online-Videokonferenzanwendungen neue Methoden zur Datenerhebung, die jedoch bisher nur selten untersucht wurden. In diesem Artikel berichten wir über die Erfahrungen von 12 Doktorand*innen mit der Durchführung von Fokusgruppen unter Verwendung von Zoom im Rahmen eines Kurses zu qualitativen Interviewmethoden. Wir reflektieren Chancen und Herausforderungen, die wir als Moderator*innen und Teilnehmer*innen bei der Nutzung von Zoom erlebten z.B. bei der Vorbereitung oder in Bezug auf Rapport, die Einbindung anderer digitaler Tools und von Internetverbindungen. Zusammenfassend lässt sich sagen, dass die Durchführung von Online-Fokusgruppen unter Verwendung von Zoom insgesamt eine positive Erfahrung war und mit Face-to-Face-Fokusgruppen vergleichbar ist. Möglichkeiten der Teilnehmer*innenrekrutierung, die Sicherheitsmerkmale von Zoom und die Nutzung von Zoom und allgemeiner neuen Technologien sollten auch jenseits der Pandemie weiter erforscht werden.As the COVID-19 pandemic swept through the world, it forced many people to adapt to an online-based routine, including qualitative researchers looking for alternative ways to collect meaningful data. While focus groups are traditionally conducted in-person, advances with online videoconferencing applications present a new method to collect data, however, few studies have explored this. In this article we present 12 doctoral students' experiences with conducting focus groups using the videoconferencing application Zoom during a qualitative methods course on interviewing methods. Through this self-study qualitative analysis, participants reflected on the opportunities and challenges experienced as both moderators and participants using Zoom including: preparation, rapport, incorporating other digital tools, and internet connectivity. In conclusion, doing focus groups online using Zoom was a positive experience overall and comparable to in-person focus groups for collecting qualitative data, despite the introduction of technology. More research on participant recruitment, new technology, Zoom's security features, and Zoom's use outside of a pandemic should be further explored

    COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

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    BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    Veridical mapping in the development of exceptional autistic abilities

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    Superior perception, peaks of ability, and savant skills are often observed in the autistic phenotype. The enhanced perceptual functioning model (Mottron et al., 2006a) emphasizes the increased role and autonomy of perceptual information processing in autistic cognition. Autistic abilities also involve enhanced pattern detection, which may develop through veridical mapping across isomorphic perceptual and nonperceptual structures (Mottron et al., 2009). In this paper, we elaborate veridical mapping as a specific mechanism which can explain the higher incidence of savant abilities, as well as other related phenomena, in autism. We contend that savant abilities such as hyperlexia, but also absolute pitch and synaesthesia, involve similar neurocognitive components, share the same structure and developmental course, and represent related ways by which the perceptual brain deals with objective structures under different conditions. Plausibly, these apparently different phenomena develop through a veridical mapping mechanism whereby perceptual information is coupled with homological data drawn from within or across isomorphic structures. The atypical neural connectivity characteristic of autism is consistent with a developmental predisposition to veridical mapping and the resulting high prevalence of savant abilities, absolute pitch, and synaesthesia in autism

    The study of the far right and its three E’s: why scholarship must go beyond Eurocentrism, Electoralism and Externalism

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    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

    No full text

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

    No full text
    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press
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