Topic Domain Research Articles

A historical review and bibliometric analysis of research on fracture nonunion in the last three decades

BackgroundNonunion continues to be the most frequent and challenging complication to treat following fracture fixation. Herein, we carried out a bibliometric analysis aiming to identify the key researchers, centres and research trends developed during the past 30 years in this important clinical condition.MethodsThe Science Citation Index Expanded database and the Web of Science Core Collection were interrogated for manuscripts published between 1990 and 2019 in the topic domain, utilising title, abstract, author keywords and KeyWords Plus. Overall, such citation indicators were used as TCyear, Cyear and CPPyear to help analyse the identified manuscripts.ResultsOver the prespecified period, there was a steady increase in the number of articles published in fracture nonunion. In total, 12 languages were the primary languages in the documents, with English being the most prevalent. The CPP sharply increased to reach a plateau in three full years and up to a peak in ten full years. A total of 8976 nonunion-related articles in Science Citation Index Expanded (SCI-EXPANDED) were published in 790 journals. The 8976 articles were published by 26,079 authors among 101 different countries. There is a slightly fluctuating steady increase of articles from 116 in 1991 to 201 in 2003, and thereafter, the number of articles sharply increased to reach a plateau in 2015. Seven possible main research foci in nonunion-related research were identified including: epidemiology, classification, aetiology, diagnosis/prediction, treatment modalities, functional outcomes and health economics.ConclusionsThis bibliometric analysis revealed information on citation number, publication outputs, categories, journals, institutions, countries, research highlights and tendencies. The current research activity on fracture nonunion identified key opinion leaders and leading research institutions focusing on this important clinical condition. It is hoped that the informed included will aid to guide research work in the foreseeable future.

National public health institutes: A scoping review

ABSTRACT During the last century, national public health institutes emerged to address enduring and emergency public health challenges. Previous outbreaks often compelled countries to establish national institutes of public health. Despite historic legacies and contributions to public health, no review of this literature has been published. The aim of this scoping review is to provide an overview of this literature and map characteristics including format, authorship, geographic focus, methods, language, focal topic and public health capacity building domains. The scoping review was guided the Arksey and O’Malley methodological framework and utilised the PRISMA-ScR checklist. A systematic search of Medline OVID and Scopus databases yielded 5731 records. In total, 43 articles met the eligibility criteria. Articles were published in English, Spanish, French and Russian and included perspectives from over 20 countries in Africa, Europe, North America and South America. Three reported methods or collected primary data. Findings reveal a longstanding international interest in leveraging national institutes to address complex public health challenges. Lack of studies reporting methods reveals the need for future research utilising robust methodology. Several articles recommend investment in national public health institutes as a strategy to respond to crises and strengthen countries’ public health systems.

An Overviuw Of The Teaching And Learning Process Basic Programming In Algorithm And Programming Courses

In this paper, we review the literature related to computer programming learning, where Algorithms and Programming are the topic domains of the Informatics and Computer science clusters. There are 4 competencies in learning outcomes, such as: 1) understand algorithmic concepts; 2) master algorithm concepts and principles; 3) master programming language concepts; and 4) master programming languages and algorithms. The main focus of this review is on beginner programming and topics related to student difficulties in learning programming. Various problems experienced by beginners were identified from the literature to some of the solutions offered by researchers.

Systematic Mapping Study on Energy Optimization Solutions in Smart Building Structure: Opportunities and Challenges

Currently building structure sector takes lead in energy consumption across the globe and is expected to increase by 2% every year. Consequently, being a major energy consumer, it's required to make effort for energy conservation as well. However, this energy conservation has to be in line with occupant thermal comfort. Recent advancement in the Internet of Things concepts attempts to transform residential areas into an energy-aware environment with aimed to reduce energy waste and automate home activities. To identify research trends and challenges in this area the study adopted the Kincham approach to performing review analysis. Our study reveals despite the benefit of this innovation, only resident with two or more unit building structure is mostly benefiting from this opportunity. Out of 94 studies considered 67 are proposed solutions. To find the trend of the research we classified the proposed solution into the topic domain (architecture, algorithm, platform, framework, and model) and it shows that trends moving toward proposing an algorithm to optimize energy consumption in the smart building structure. The study also performed an analytical evaluation on a proposed solution to assess their quality attributes and our evaluation reveals none of the studies considered security in their solution. Finally, the study identifies challenges that bottleneck the development and acceptance of this innovation into societies.

Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.

Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making. Using thematic analysis, we describe challenges, considerations, and solutions for algorithm design decisions in a collaboration between health services researchers, clinicians, and data scientists. We use the design process of an RL algorithm for a mobile health study "DIAMANTE" for increasing physical activity in underserved patients with diabetes and depression. Over the 1.5-year project, we kept track of the research process using collaborative cloud Google Documents, Whatsapp messenger, and video teleconferencing. We discussed, categorized, and coded critical challenges. We grouped challenges to create thematic topic process domains. Nine challenges emerged, which we divided into 3 major themes: 1. Choosing the model for decision-making, including appropriate contextual and reward variables; 2. Data handling/collection, such as how to deal with missing or incorrect data in real-time; 3. Weighing the algorithm performance vs effectiveness/implementation in real-world settings. The creation of effective behavioral health interventions does not depend only on final algorithm performance. Many decisions in the real world are necessary to formulate the design of problem parameters to which an algorithm is applied. Researchers must document and evaulate these considerations and decisions before and during the intervention period, to increase transparency, accountability, and reproducibility. clinicaltrials.gov, NCT03490253.

On the challenges of predicting microscopic dynamics of online conversations

To what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning algorithms. The model is evaluated using datasets that span two topical domains (cryptocurrency and cyber-security) and two platforms (Reddit and Twitter). We show that it is able to predict both macroscopic features of the final trees and near-future microscopic events with moderate accuracy. However, predicting the macroscopic structure of conversations does not guarantee an accurate reconstruction of their microscopic evolution. Our model’s limited performance in long-range predictions highlights the challenges faced by generative models due to the accumulation of errors.

Using a language socialization framework to explore Chinese Students’ L2 Reticence in English language learning

Chinese students’ reticence in English learning has been widely observed in language classrooms. While a number of prior studies have made a significant contribution to understanding Chinese students’ L2 reticence, greater clarity would benefit the field's understanding of L2 reticence, in terms of L2 reticence as a classroom phenomenon and how learners’ agentic effort manifests in reticent ways of language learning. To bridge this gap, in the present study we set out to investigate Chinese students’ L2 interactional reticence drawing insights from a language socialization perspective and using an ethnographic design. In doing so, this study demonstrated the mechanisms of language socialization in an English class in China, and provided alternative ways of understanding how the students appeared to become reticent learners of English. These results suggest a more nuanced understanding of L2 reticence. Based on these findings, we discuss the potential of the language socialization approach in researching L2 reticence and implications for research in this topical domain.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology.

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Arabic sentiment analysis about online learning to mitigate covid-19

Abstract The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases.

Sleep Neurology's Toolkit at the Crossroads: Challenges and Opportunities in Neurotherapeutics Lost and Found in Translation.

We find ourselves at our present crossroads with a well-traveled toolkit, perhaps too well worn but with aspirational hopes and dreams for the field of sleep neurotherapeutics. This volume is organized thematically into six topical domains that parallel the major subspecialty areas of contemporary clinical sleep neurology practice, as well as novel directions and opportunities. The issue begins with an overview of the central disorders of hypersomnolence, including narcolepsy, idiopathic hypersomnia and other hypersomnia disorders, and the related use of the entire broad range of stimulant and wake-promoting pharmacotherapies. Next, the range of behavioral therapies, application of light and light restriction and melatonin therapies, and hypnotic pharmacotherapies useful in insomnia and circadian sleep–wake rhythm disorders are reviewed, followed by an overview of treatment options for sleep-related breathing disorders including positive airway pressure and the novel approach of hypoglossal neurostimulation for obstructive sleep apnea. The parasomnias and sleep-related movement disorders, including NREM disorders of arousal, REM parasomnias (nightmares and isolated sleep paralysis and idiopathic/isolated REM sleep behavior disorder, and restless legs syndrome are then discussed, and the applications of sleep neurotherapeutics in sleep and neurological disease are reviewed, including neurodevelopmental, epileptic, autoimmune encephalopathies, and neurodegenerative diseases. Last, the novel directions and opportunities in sleep neurology offered by cannabinoid therapies and machine learning/artificial intelligence methodology conclude this comprehensive survey of contemporary sleep neurology. We hope that you find this volume to be a useful and inspirational support tool for the work that matters most, your care of all our sleep neurology patients in the clinics.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13311-021-01032-7.

A new approach to (key) keywords analysis: Using frequency, and now also dispersion

A widely-used method in corpus-linguistic approaches to discourse analysis, register/text type/genre analysis, and educational/curriculum questions is that of keywords analysis, a simple statistical method aiming to identify words that are key to, i.e. characteristic for, certain discourses, text types, or topic domains. The vast majority of keywords analyses relied on the same statistical measure that most collocation studies are using, the log-likelihood ratio, which is performed on frequencies of occurrence in two corpora under consideration. In a recent paper, Egbert and Biber (2019) advocated a different approach, one that involves computing log-likelihood ratios for word types based on the range of their distribution rather than their frequencies in the target and reference corpora under consideration. In this paper, I argue that their approach is a most welcome addition to keywords analysis but can still be profitably extended by utilizing both frequency and dispersion for keyness computations. I am presenting a new two-dimensional approach to keyness and exemplifying it on the basis of the Clinton-Trump Corpus and the British National Corpus.

A Mixed-Methods Systematic Review of the Impacts of Coronavirus on Society and Culture.

Little is understood of the social and cultural effects of coronaviruses such as coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS-CoV). This systematic review aims to synthesize existing findings (both qualitative and quantitative) that focus on the social and cultural impacts of coronaviruses in order to gain a better understanding of the COVID-19 pandemic. Utilizing a predetermined search strategy, we searched CINAHL, PsycINFO, PubMed, and Web of Science to identify existing (qualitative, quantitative, and mixed-methods) studies pertaining to the coronavirus infections and their intersection with societies and cultures. A narrative synthesis approach was applied to summarize and interpret findings of the study. Stemming from SARS outbreak in 2003, qualitative and quantitative findings (twelve adopted quantitative methods and eight exclusively used qualitative methods) were organized under five topical domains: governance, crisis communication and public knowledge, stigma and discrimination, social compliance of preventive measures, and the social experience of health workers. The selected studies suggest that current societies are not equipped for effective coronavirus response and control. This mixed-methods systematic review demonstrates that the effects of coronaviruses on a society can be debilitating.

소셜 빅데이터 연구를 위한 커플 파트너십 온톨로지 개발

The purpose of this study was to develop an ontology of intimate partnerships that can be used as a framework for collecting, processing, and analyzing big data of online posts written in Korean. An ontology is a group of words related to a certain topic domain that are grouped into a stratified structure. The scope of our ontology is various intimate partnerships and non-partnerships including singlehood, dating, cohabitation, domestic partnership, living apart together, (re)marriage, divorce, widowhood in terms of the formation, continuation, and dissolution of such partnerships. Based on Noy and McGuinness’ (2001) guidelines, we developed an ontology with two overarching classes: the dimensions of partnerships and the dimensions surrounding partnerships. The dimensions of partnerships included five classes: (a) dating, (b) marriage, (c) couple life, (d) voluntary singlehood and cohabitation, and (e) divorce and remarriage. The dimensions surrounding partnerships had four classes: (a) daily life, (b) micro factors, (c) macro factors, and (d) others. We also developed a diagram of the ontology to help understand the overall structure of the ontology. Future researchers can use our ontology of intimate partnerships as a conceptual framework and methodological tool to explore the trend of intimate partnerships in Korea. Our ontology can also be expanded to a broader topic domain or adapted to other cultures and languages.

Teaching psychiatric pharmacogenomics effectively: Evaluation of a novel interprofessional online course

AbstractIntroductionThe application of pharmacogenomic (PGx) testing to guide psychotropic use is increasing, but there is a lack of training opportunities for health care providers designed specifically around the unique issues impacting the use and interpretation of PGx in psychiatry. Furthermore, providing such education online greatly improves the accessibility of such training.ObjectiveThe objective was to design and evaluate a live online continuing education (CE) course on psychiatric PGx for health care providers.MethodsA multidisciplinary team of experts in psychiatry, PGx, and medical education research designed a three‐session online course to discuss key psychiatric PGx topics ranging from fundamental background material, selecting a test, and interpreting and applying results. A deliberate design of the program was to offer the course three times, in order to allow successive improvements to each iteration. To evaluate the course, pre‐ and post‐surveys were developed with the intent of capturing change in the four Dixon levels of CE evaluation on attendee perceptions and opinions, knowledge and attitudes, and impact on clinical practice and patient outcomes.ResultsIn the third course iteration, 32 health care providers registered. Among survey respondents, the course materials, speakers, organization, and online format were reviewed favorably by the majority of attendees. Perceived knowledge of PGx increased in every identified PGx topic domain and was largely reflected by improved knowledge assessment scores. A limited number of survey participants also indicated changes in clinical behavior and patient outcomes as a direct result of the material covered in this course.ConclusionsAmong survey respondents, this novel online PGx course proved effective at increasing provider understanding and confidence in key psychiatric PGx topic areas. It also demonstrated the viability of the online format, a vital characteristic to allow for future widespread dissemination.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Preschoolers’ choices of television characters as sources of information: Effects of character type, format, and topic domain

Educational television (TV) for preschoolers often features unrealistic (anthropomorphic and/or animated) characters, and it is unclear how children perceive such characters as sources of information. In this study, 92 3- to 5-year-olds saw images of characters and chose which would be best for learning about six topics across three domains: animal biology, human biology, and socioemotional. Children were randomized to one of three conditions in which they chose between paired images of (1) live-action humans versus animated humans (i.e., holding character type constant and varying format), (2) animated humans versus animated anthropomorphic animals (i.e., holding format constant and varying character type), or (3) live-action humans versus animated anthropomorphic animals (i.e., most realistic vs. least realistic character choices). Results indicated that children chose animated characters over live-action characters (Conditions 1 and 3). They did not choose humans significantly more often than animals even for human biology and socioemotional lessons (Conditions 2 and 3). However, in Condition 2, they chose animals significantly more often than humans for animal biology lessons. In sum, children showed little sign of prioritizing realism. The study adds children’s perspectives to the literature on the effects of reality cues on early learning from educational TV.

Optimizing virtual and distance learning during an emergency and beyond.

The novel coronavirus 2019 (COVID-19) pandemic has forced program directors of sleep medicine fellowship programs, and other clinical training programs, to immediately transition longstanding face-to-face clinical and didactic instruction to virtual formats. The effects of this sudden transition to distance learning affect multiple aspects of training, from recruitment to patient care, scholarly activity, and well-being. Clinical educators must also understand how to consider and maintain equity while implementing distance learning strategies. Resources were collected from multiple sites that are openly accessible to sleep medicine educators. These resources are presented within their topic domains to provide guidance on how to effectively implement distance learning strategies into a clinical training program. Links to helpful resources are provided for each of the following topics: virtual clinical care, didactic delivery in a virtual clinical learning environment, generating scholarship via distance learning, well-being in the setting of distance learning, virtual interviews, and equity in a virtual clinical learning environment. Clinical training programs, including sleep medicine fellowships, can utilize virtual and distance learning methodologies to deliver, and even enhance, currently existing curricula. The widespread adoption of distance learning strategies opens new opportunities for educational innovation and collaboration among training programs.

Responses to Concerning Posts on Social Media and Their Implications for Suicide Prevention Training for Military Veterans: Qualitative Study.

BackgroundA “concerning post” is a display of a user’s emotional crisis on a social media platform. A better understanding of concerning posts is relevant to suicide prevention, but little is known about social media users’ attitudes and responses to concerning posts. Military veterans in the United States are disproportionately affected by suicide, often use social media, and may have exposure to individuals with elevated suicide risk via concerning posts.ObjectiveThe objective of the study was (1) to obtain insight into whether and how US military veterans respond to members of their social network on social media (ie, “friends”) who are experiencing substantial emotional distress, and (2) to identify potential interventions that could assist in users’ response to concerning posts.MethodsWe recruited veterans through Facebook and conducted semistructured interviews with 30 participants between June and December 2017. We used a summary template for rapid analysis of each interview, followed by double-coding using a codebook based on topic domains from the interview guide. Members of the research team met regularly to discuss emerging patterns in the data, generate themes, and select representative quotes for inclusion in the manuscript.ResultsVeterans were reluctant to disclose emotional and health issues on Facebook, but they were open to reaching out to others’ concerning posts. There was a complex calculus underlying whether and how veterans responded to a concerning post, which involved considering (1) physical proximity to the person posting, (2) relationship closeness, (3) existing responses to the post, and (4) ability to maintain contact with the person. Veterans desired additional training, backed by community-based veteran organizations, in how to respond to concerning posts from peers.ConclusionsThere is a need to incorporate features that will help veterans effectively respond to concerning posts from peers into suicide prevention training and to expand access for veterans to such training.

Guidelines for caregivers and healthcare professionals on speaking to children about overweight and obesity: a systematic review of the gray literature.

Caregivers and healthcare professionals (HPs) are increasingly concerned about childhood obesity. A critical consideration of caregivers and HPs is discussing weight status without provoking disordered eating. Given the complexity of these interacting concerns, major health advocacy groups have independently published guidelines for having conversations with children about overweight/obesity. The current investigation represented the first-ever systematic review of these guidelines to analyze their content, consistency, actionability, or scientific support. To conduct a systematic review, the authors performed a web-based search using the terms parent/HP, guidelines, child, and overweight/obesity that identified 59 guidelines on childhood obesity broadly, of which 13 provided explicit direction on how the caregiver or HP should approach a conversation about overweight with a child. Within these 13 guidelines, nine topic domains were identified: attitude modeling (covered by 31% of guidelines), behavior modeling (61%), dietary recommendations (54%), physical activity (46%), body acceptance and self-esteem (69%), conversation advice (92%), contact with HP (46%), talking about "weight" versus "overall health" (54%) and external factors (e.g., bullying, media) (54%). Although guidelines presented similar content, several inconsistencies in recommendations emerged. Notably, only three of the 13 guidelines referenced any scholarly sources and only a small minority of advice was actionable by caregivers or HPs. It is evident that guidelines for caregivers and HPs on speaking to children about obesity offer inconsistent advice, minimally based on empirical evidence. Future guidelines should aim to unify their messages for caregivers and HP and be better supported by empirical data.

Robotic surgery: training, competence assessment and credentialing

As a topical domain, robotic surgery is a fast moving area of both research and innovation, but there seems to be a dearth of information about training and curricula. Looking at training and future practice for robotic surgery, the authors examine the current situation but also consider potential future directions in terms of mandatory curricula (for multiple specialties) and assessment of competence. The article also covers some general areas of implementation and learning curves for new technologies. An international consensus on robotic surgical training is required to guarantee regulated uptake of this expanding area of innovation.