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Introduction The utilization of internet-based data collection in mental health research has gained popularity for its convenience and affordability. However, concerns often arise regarding the validity and reliability of data collected via the internet. The Autism Spectrum Quotient (AQ) is a self-report questionnaire to measure the traits associated with autism spectrum disorder (Baron-Cohen et al. J Autism Dev Disord, 2001; 31 5-17) and the online usage of AQ is common and conducted with large numbers of participants across many studies. However, the effect of using internet-based data collection for AQ rather than conventional paper-based procedures is unknown. Objectives To address this issue, we conducted a study comparing the effectiveness of internet-based and paper-based data collection procedures for both the AQ and Depression Anxiety Stress Scale-21 (DASS-21, Lovibond & Lovibond, Behav Res Ther 1995; 33 335–343), which is also a prevalent mental health measurement in the literature and often used for online data collection (Zlomke, Comput Hum Behav 2009; 25 841-843). In addition, to compare internet-based and paper-based methods more fully, we included another variable (type of supervision) where a researcher was either present or absent during the completion of the questionnaires. Methods A power analysis was conducted, and a minimum of 90 participants were needed to reach a medium effect size of .30 with an adequate power of .80 at a= .05. Accordingly, 96 participants were used and randomly assigned across 4 data collection groups: internet-based (supervision, no supervision) and paper-based (supervision, no supervision). In addition to a Demographic Form, AQ, and DASS-21 were used to obtain the data. Three independent variables were used in the current study: type of presentation (internet-based and paper-based) and type of supervision as between factors, and type of assessment as a within factor. Results Using a 2 x 2 x 2 mixed design ANOVA, no significant main effects were found for any independent variables (all p > .33) or interaction (all ps > .17). Conclusions The results of using AQ and DASS-21 were not altered by using internet-based or paper-based data collection procedures, suggesting that both methodologies are equally valid for this purpose. Moreover, these effects were also unaffected by the presence or absence of a researcher during data collection, suggesting that supervision by an authoritative figure does not alter the responses made. Disclosure of Interest None Declared

This document contains abstracts of papers presented at an academic conference. The papers cover a wide range of topics related to economics, history, and social development. Each paper presents empirical evidence and analysis to shed light on specific phenomena or historical events. The research is conducted using different methodologies and data sources, providing valuable insights into these complex subjects. The summaries provide a brief overview of each paper's main findings and contribute to the understanding of various social and economic phenomena. [Extracted from the article]

The 2022 anti–zero Covid White Paper protests that erupted in China and its diaspora demonstrated the intertwinement of politics and performance in contemporary China. The symbolic dimensions of blank sheets of A4 paper and other performative tactics used in these demonstrations exemplify the shifting roles of performance art in the field of contemporary Chinese art. [ABSTRACT FROM AUTHOR]

Abstract Background Climate change is one of the greatest threats to health that societies face and can adversely affect mental health. Given the current lack of a European consensus paper on the interplay between climate change and mental health, we signal a need for a pan-European position paper about this topic, written by stakeholders working in mental health care. Methods On behalf of the European Psychiatric Association (EPA), we give recommendations to make mental health care, research, and education more sustainable based on a narrative review of the literature. Results Examples of sustainable mental healthcare comprise preventive strategies, interdisciplinary collaborations, evidence-based patient care, addressing social determinants of mental health, maintaining health services during extreme weather events, optimising use of resources, and sustainable facility management. In mental health research, sustainable strategies include investigating the impact of climate change on mental health, promoting research on climate change interventions, strengthening the evidence base for mental health-care recommendations, evaluating the allocation of research funding, and establishing evidence-based definitions and clinical approaches for emerging issues such as ‘eco-distress’. Regarding mental health education, planetary health, which refers to human health and how it is intertwined with ecosystems, may be integrated into educational courses. Conclusions The EPA is committed to combat climate change as the latter poses a threat to the future of mental health care. The current EPA position paper on climate change and mental health may be of interest to a diverse readership of stakeholders, including clinicians, researchers, educators, patients, and policymakers.

Racial and ethnic disparities are well described in paediatric cardiac critical care outcomes. However, understanding the mechanisms behind these outcomes and implementing interventions to reduce and eliminate disparities remain a gap in the field of paediatric cardiac critical care. The Pediatric Cardiac Critical Care Consortium (PC 4 ) established the Equity, Diversity, and Inclusion (EDI) Committee in 2020 to promote an equity lens to its aim of improving paediatric cardiac critical care quality and outcomes across North America. The PC 4 EDI Committee is working to increase research, quality improvement, and programming efforts to work towards health equity. It also aims to promote health equity considerations in PC 4 research. In addition to a focus on patient outcomes and research, the committee aims to increase the inclusion of Black, Indigenous, and People of Color (BIPOC) members in the PC 4 collaborative. The following manuscript outlines the development, structure, and aims of the PC 4 EDI Committee and describes an analysis of social determinants of health in published PC 4 research.

This white paper provides clinicians and hospital leaders with practical guidance on the prevention and control of viral respiratory infections in the neonatal intensive care unit (NICU). This document serves as a companion to Centers for Disease Control and Prevention Healthcare Infection Control Practices Advisory Committee (HICPAC)'s "Prophylaxis and Screening for Prevention of Viral Respiratory Infections in Neonatal Intensive Care Unit Patients: A Systematic Review." It provides practical, expert opinion and/or evidence-based answers to frequently asked questions about viral respiratory detection and prevention in the NICU. It was developed by a writing panel of pediatric and pathogen-specific experts who collaborated with members of the HICPAC systematic review writing panel and the SHEA Pediatric Leadership Council to identify questions that should be addressed. The document has been endorsed by SHEA, the American Hospital Association (AHA), The Joint Commission, the Pediatric Infectious Diseases Society (PIDS), the Association for Professionals in Infection Control and Epidemiology (APIC), the Infectious Diseases Society of America (IDSA), and the National Association of Neonatal Nurses (NANN).

This study traces the evolution of computer-assisted language learning (CALL) by investigating published research articles (RAs) in four major CALL journals: ReCALL, Computer Assisted Language Learning, Language Learning & Technology, and CALICO Journal. All 2,397 RAs published over four decades (1983-2019) were included in the pool of data, and the Google Scholar citation metric was adopted to assess the impact of the papers. By selecting the top 15% of widely cited papers from each individual year, we minimized the time bias between years, enabling a balanced narration of the history of CALL through a representative dataset of 426 high-impact RAs. To identify the evolution of research trends, the contexts, methodologies, theoretical underpinnings and research foci of all 426 RAs were investigated using NVivo 12 and AntConc. The analysis of the data yielded encouraging results such as the upward trend in the number of publications and the international reach of CALL in the last two decades, the physical or virtual presence of language learners with diverse language profiles, and the growing tendency to triangulate methodology for increased complexity. However, long-standing issues such as the heavy reliance on traditional research contexts, poor reporting practices of basic demographic information, the large number of atheoretical papers and the concentration on a limited number of research foci continue to pose challenges in CALL research. Based on the findings, the paper suggests solutions for the controversies and addresses key issues for future research in CALL. [ABSTRACT FROM AUTHOR]

This document is a summary of selected papers from the 5th International Joint Conference on Rules and Reasoning (RuleML+RR 2021). The conference brought together researchers and practitioners in the field of rule technologies and reasoning. The papers included in this issue cover a range of topics, including high-utility pattern mining, rehabilitation scheduling problems, meta-reasoning over ontology, and distributed subweb specifications for traversing the web. These papers demonstrate the diversity and quality of the contributions to the conference. The document also acknowledges the reviewers and organizers who made the conference and this special issue possible. [Extracted from the article]

This Position Paper from the Academy of Nutrition Sciences is the third in a series which describe the nature of the scientific evidence and frameworks that underpin nutrition recommendations for health. This paper focuses on evidence which guides the application of dietary recommendations for individuals. In some situations, modified nutrient intake becomes essential to prevent deficiency, optimise development and health, or manage symptoms and disease progression. Disease and its treatment can also affect taste, appetite and ability to access and prepare foods, with associated financial impacts. Therefore, the practice of nutrition and dietetics must integrate and apply the sciences of food, nutrition, biology, physiology, behaviour, management, communication and society to achieve and maintain human health. Thus, there is huge complexity in delivering evidence-based nutrition interventions to individuals. This paper examines available frameworks for appraising the quality and certainty of nutrition research evidence, the development nutrition practice guidelines to support evidence implementation in practice and the influence of other sources of nutrition information and misinformation. The paper also considers major challenges in applying research evidence to an individual and suggests consensus recommendations to begin to address these challenges in the future. Our recommendations target three groups; those who deliver nutrition interventions to individuals, those funding, commissioning or undertaking research aimed at delivering evidence-based nutrition practice, and those disseminating nutritional information to individuals. [ABSTRACT FROM AUTHOR]

The document is a summary of the journal article "Selected Papers from Datalog 2.0 2022" published in the Theory & Practice of Logic Programming. The article discusses the 4th International Workshop on the Resurgence of Datalog in Academia and Industry (Datalog 2.0 2022), which aimed to bring together researchers, implementors, and users of Datalog. The workshop received a significant number of submissions, and 21 papers were accepted for publication. The article also highlights the inclusion of an invited talk by Reinhard Pichler and emphasizes the collaborative and vibrant nature of the Datalog community. The document concludes by expressing anticipation for the future contributions and innovations in Datalog research. [Extracted from the article]

An introduction is presented in which the editor discusses various articles on topics including ENT specialist training in the United Kingdom, providing valuable insights for aspiring ENT surgeons regarding portfolio building, competitiveness of the process, and strategies for success.

In this paper, we study newspaper partisan behavior and content, which we measure using coverage of and commentary on partisan activities, institutions, and actors. We use this measure to describe the levels of relative partisan behavior during the period 1880 to 1900, and to describe changes over the period 1880 to 1980. We find that, on average, newspapers were initially highly partisan, but gradually became less partisan over time. Importantly, we find as much change after the 1910s as before, which contributes to the existing literature that focuses on changes in the nineteenth and early twentieth centuries. We also investigate words and phrases that had negative or positive partisan connotations in particular periods. Finally, we examine whether some of the common hypotheses offered in the literature can account for the changes. The initial findings suggest that these explanations can only account for part of the decline. [ABSTRACT FROM AUTHOR]

Recent studies on technology-mediated task-based learning have shown the impact of task design and modality on English as a foreign language (EFL) learning. However, it is unclear what effect technology-mediated tasks have on learners' English language skills. This paper presents a classroom-based study that showed how using technology-mediated tasks impacted students' learning experiences and fostered the development of specific speaking and writing subskills in an EFL secondary education context. Forty-two EFL intermediate learners completed two speaking and two writing tasks from the Cambridge B2 First exam using mobile devices. The participants were divided into a pen-and-paper group (N = 21) and an iPad group (N = 21). Learning outcomes were measured using a pre-test/post-test design with a statistical comparison of ratings across tasks. A qualitative content analysis of lesson observations and student and teacher interviews served as an additional dataset to shed light on learners' experiences. Descriptive statistics revealed that the iPad group achieved higher scores in pronunciation and accuracy (speaking) and essay organisation features (writing). Tasks involving the active use of the tool for content creation, rehearsing speaking performances, and accessing authentic materials were the most successful among students. [ABSTRACT FROM AUTHOR]

Exploring diverse strategies of process-focused research from historical reconstructions of period practices to staged intimacy coaching, seven new titles offer a representative but not exhaustive overview of the expanding field of rehearsal studies. [ABSTRACT FROM AUTHOR]

Multilevel regression and post-stratification (MRP) is a popular use of hierarchical models in political science. Multiple papers have suggested that relying on machine learning methods can provide substantially better performance than traditional approaches that use hierarchical models. However, these comparisons are often unfair to traditional techniques as they omit possibly important interactions or nonlinear effects. I show that complex ("deep") hierarchical models that include interactions can nearly match or outperform state-of-the-art machine learning methods. Combining multiple models into an ensemble can improve performance, although deep hierarchical models are themselves given considerable weight in these ensembles. The main limitation to using deep hierarchical models is speed. This paper derives new techniques to further accelerate estimation using variational approximations. I provide software that uses weakly informative priors and can estimate nonlinear effects using splines. This allows flexible and complex hierarchical models to be fit as quickly as many comparable machine learning techniques. [ABSTRACT FROM AUTHOR]

Most countries in Africa deployed digital solutions to monitor progress in rolling out COVID-19 vaccines. A rapid assessment of existing data systems for COVID-19 vaccines in the African region was conducted between May and July 2022, in 23 countries. Data were collected through interviews with key informants, identified among senior staff within Ministries of Health, using a semi-structured electronic questionnaire. At vaccination sites, individual data were collected in paper-based registers in five countries (21.7%), in an electronic registry in two countries (8.7%), and in the remaining 16 countries (69.6%) using a combination of paper-based and electronic registries. Of the 18 countries using client-based digital registries, 11 (61%) deployed the District Health Information System 2 Tracker, and seven (39%), a locally developed platform. The mean percentage of individual data transcribed in the electronic registries was 61% ± 36% standard deviation. Unreliable Internet coverage (100% of countries), non-payment of data clerks' incentives (89%), and lack of electronic devices (89%) were the main reasons for the suboptimal functioning of digital systems quoted by key informants. It is critical for investments made and experience acquired in deploying electronic platforms for COVID-19 vaccines to be leveraged to strengthen routine immunization data management. [ABSTRACT FROM AUTHOR]

Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High-Utility Pattern Mining; in particular, we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

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