Hasil untuk "English literature"

Ivan D Florez, Markus Klimek, Ginna Cabra-Bautista et al.

Introduction Intravenous fluids are essential components of perioperative care, supporting intravascular volume, acid–base balance and electrolyte homeostasis. Despite extensive research in adult surgical populations, paediatric-specific evidence remains limited, and clinical practice frequently relies on extrapolated adult-based recommendations. This gap is particularly relevant in paediatric non-cardiac surgery, where fluid choice may influence key physiological outcomes such as acid–base status, electrolyte balance, renal function and haemodynamic stability. Given the heterogeneity of study designs, perioperative phases, age groups and reported outcomes in the paediatric literature, a comprehensive synthesis of the existing evidence is needed before a systematic review can be undertaken.Methods and analysis We will conduct this scoping review following the methodological guidance of the Joanna Briggs Institute Manual for Evidence Synthesis, and the reporting will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guideline.This scoping review will map existing evidence on perioperative intravenous fluid management in paediatric patients (<18 years) undergoing elective non-cardiac surgery in outpatient and inpatient settings. Eligible study designs will include randomised trials, observational studies and systematic reviews. A comprehensive search will be developed with a medical librarian and applied to MEDLINE (PubMed), Ovid, Embase, Web of Science, CENTRAL, Google Scholar and ClinicalTrials.gov, with no date restrictions and limited to English, Spanish and German.Eligibility is framed using participants, concept and context: paediatric patients (<18 years) undergoing non-cardiac surgery; concepts related to preoperative fasting/replacement period, intraoperative period and postoperative period up to 24 hours, intravenous fluid management for maintenance/replacement; and hospital/outpatient surgical settings. Study selection and data charting will follow established scoping review methodology. Data will be synthesised descriptively using narrative and tabular formats. No meta-analysis or formal risk-of-bias appraisal is planned, consistent with scoping review methodology.Ethics and dissemination This scoping review involves no primary data collection and relies exclusively on published literature; therefore, formal ethical approval is not required. The protocol received administrative approval from the Comité de Ética para la Investigación Científica of Universidad del Cauca (approval no. 6553, 11 June 2025). Findings will be disseminated through peer-reviewed publications, conference presentations and targeted communication with paediatric anaesthesia and surgical communities.

Dian Fajrina

Themba Ginindza, Javan Solomon Okello, Julius N Odhiambo

Introduction The COVID-19 pandemic amplified the need for robust multisectoral coordination; yet the specific mechanisms, benefits and challenges of such collaboration particularly in low- and middle-income countries (LMICs) remain poorly synthesised. This review aims to delineate the key elements, benefits, challenges and improvement strategies of multisectoral coordination during COVID-19 and to compare patterns between LMICs and high-income countries (HICs).Methods and analysis Eligible studies will include empirical qualitative, quantitative or mixed-methods research published in English between 1 January 2020 and 15 August 2024 that examines formal coordination mechanisms (eg, task forces, public-private partnerships, inter-agency committees) within the context of COVID-19. Searches will be conducted across PubMed, EBSCOhost, Emerald Insight, Google Scholar and selected grey-literature repositories. Citation chaining will be employed to identify additional sources.Two reviewers will independently screen all records using Covidence, applying pre-piloted eligibility criteria to 5% of citations and proceeding only if inter-rater reliability achieves κ≥0.70. Data will be extracted into a Consolidated Framework for Implementation Research (CFIR)-informed template. Qualitative data will be analysed through framework synthesis, structured by the five CFIR domains. Quantitative data will be narratively summarised and, where outcomes are sufficiently similar across at least two studies, synthesised using a fixed-effect model.Risk of bias will be assessed using Critical Appraisal Skills Programme for qualitative and Risk Of Bias In Non-randomised Studies of Interventions for non-randomised studies. Studies with serious or critical risk will be excluded from pooling. Subgroup analyses (LMIC vs HIC), sensitivity analyses (model and risk) and confidence grading using Confidence in the Evidence from Reviews of Qualitative Research and Grading of Recommendations, Assessment, Development and Evaluations will be conducted.Ethics and dissemination No primary data will be collected; thus additional Research Ethics Committee approval is unnecessary. The results will be disseminated via open-access publication, conference presentations and policy briefs for Nairobi County health stakeholders.PROSPERO registration number CRD42023466849.

Aida Kalok, Aida Kalok, Ixora Kamisan Atan et al.

Fear of childbirth (FOC) or tokophobia adversely affects women during pregnancy, delivery, and postpartum. Childbirth fear may differ across regions and cultures. We aimed to identify factors influencing the fear of childbirth among the Asian population. A systematic literature search was performed using the PubMed, Scopus, and Web of Science databases in November 2023. Original articles in English with research conducted in Asian countries were included. The independent factors associated with childbirth fear, from the relevant studies were identified and discussed. Forty-six papers met the eligibility criteria but only 26 studies were discussed in this review. The significant factors were categorized into (1) demographics, (2) clinical, (3) healthcare service, (4) childbirth education & information, and (5) COVID-19 pandemic. The prevalence of childbirth fear among Asians ranged between 56.6 and 84.8%. Significant demographic factors included age, education, marital status, economic status, and area of residence. Greater levels of tokophobia were linked to nulliparity, unplanned pregnancy, infertility, miscarriage, and pregnancies at risk. Effective doctor-patient communication and more frequent antenatal visits were shown to alleviate maternal childbirth fear. There was consistent evidence of prenatal childbirth education’s benefit in reducing FOC. The usage of smartphone apps and prolonged exposure to electronic devices were linked to a higher degree of tokophobia. Nulliparas who received too much pregnancy-related information also reported increased childbirth fear. There was a positive correlation between maternal fear of COVID-19 infection and FOC. Keeping updated with COVID-19 information increased the maternal childbirth fear by two-fold. In conclusion, a stable economy and relationship contribute to lesser childbirth fear among Asian women. Poor maternal health and pregnancy complications were positive predictors of FOC. Health practitioners may reduce maternal childbirth through women’s education, clear communication as well as accurate information and guidance to expectant mothers. Further study is required into the content of childbirth fear among Asian women. These research findings hopefully will lead to the development of culturally adapted screening tools and interventions that reduce the burden of FOC among expectant mothers.

Anirban Halder, Rituparna Biswas, Saumen Basu

Adenoid cystic carcinoma (ACC) arising from the sphenoid sinus poses unique challenges in management due to its proximity to critical structures and late-stage presentations. We herein detail a rare case of a 46-year-old female with ACC originating from the sphenoid sinus, manifesting as progressive left-eye vision loss. After decompressive surgery, the patient received radical radiotherapy (RT), leading to significant recovery in left ptosis and improved vision in the right eye. The effectiveness of RT in treating ACC remains a topic of debate, as evidenced by divergent opinions in the literature, with only 20 comparable cases documented in English literature. This rare case provides valuable insights into the management of ACC arising from the sphenoid sinus, underscoring the pivotal role of radical RT in situations where surgical intervention alone proves inadequate.

Rena Gao, Xuetong Wu, Tatsuki Kuribayashi et al.

This study evaluates Large Language Models' (LLMs) ability to simulate non-native-like English use observed in human second language (L2) learners interfered with by their native first language (L1). In dialogue-based interviews, we prompt LLMs to mimic L2 English learners with specific L1s (e.g., Japanese, Thai, Urdu) across seven languages, comparing their outputs to real L2 learner data. Our analysis examines L1-driven linguistic biases, such as reference word usage and avoidance behaviors, using information-theoretic and distributional density measures. Results show that modern LLMs (e.g., Qwen2.5, LLAMA3.3, DeepseekV3, GPT-4o) replicate L1-dependent patterns observed in human L2 data, with distinct influences from various languages (e.g., Japanese, Korean, and Mandarin significantly affect tense agreement, and Urdu influences noun-verb collocations). Our results reveal the potential of LLMs for L2 dialogue generation and evaluation for future educational applications.

Rebecca Dorn, Christina Chance, Casandra Rusti et al.

Automated emotion detection is widely used in applications ranging from well-being monitoring to high-stakes domains like mental health and hiring. However, models often rely on annotations that reflect dominant cultural norms, limiting model ability to recognize emotional expression in dialects often excluded from training data distributions, such as African American Vernacular English (AAVE). This study examines emotion recognition model performance on AAVE compared to General American English (GAE). We analyze 2.7 million tweets geo-tagged within Los Angeles. Texts are scored for strength of AAVE using computational approximations of dialect features. Annotations of emotion presence and intensity are collected on a dataset of 875 tweets with both high and low AAVE densities. To assess model accuracy on a task as subjective as emotion perception, we calculate community-informed "silver" labels where AAVE-dense tweets are labeled by African American, AAVE-fluent (ingroup) annotators. On our labeled sample, GPT and BERT-based models exhibit false positive prediction rates of anger on AAVE more than double than on GAE. SpanEmo, a popular text-based emotion model, increases false positive rates of anger from 25 percent on GAE to 60 percent on AAVE. Additionally, a series of linear regressions reveals that models and non-ingroup annotations are significantly more correlated with profanity-based AAVE features than ingroup annotations. Linking Census tract demographics, we observe that neighborhoods with higher proportions of African American residents are associated with higher predictions of anger (Pearson's correlation r = 0.27) and lower joy (r = -0.10). These results find an emergent safety issue of emotion AI reinforcing racial stereotypes through biased emotion classification. We emphasize the need for culturally and dialect-informed affective computing systems.

Jay L. Cunningham, Adinawa Adjagbodjou, Jeffrey Basoah et al.

This scoping literature review examines how fairness, bias, and equity are conceptualized and operationalized in Automatic Speech Recognition (ASR) and adjacent speech and language technologies (SLT) for African American English (AAE) speakers and other linguistically diverse communities. Drawing from 44 peer-reviewed publications across Human-Computer Interaction (HCI), Machine Learning/Natural Language Processing (ML/NLP), and Sociolinguistics, we identify four major areas of inquiry: (1) how researchers understand ASR-related harms; (2) inclusive data practices spanning collection, curation, annotation, and model training; (3) methodological and theoretical approaches to linguistic inclusion; and (4) emerging practices and design recommendations for more equitable systems. While technical fairness interventions are growing, our review highlights a critical gap in governance-centered approaches that foreground community agency, linguistic justice, and participatory accountability. We propose a governance-centered ASR lifecycle as an emergent interdisciplinary framework for responsible ASR development and offer implications for researchers, practitioners, and policymakers seeking to address language marginalization in speech AI systems.

Eleonora Girina

Considering their important role in human life the study of emotions is of great interest for linguistic science. This article is dedicated to the analysis of the phenomenon of somatization of sadness and the explanation of differences in the levels of such somatization as reflected in language. The analysis of literature has shown that expression of sadness with the help of somatic expressions is particularly prevalent in African, South-East Asian and Australian languages. Organs that are most often associated with emotions are the heart, liver and stomach and interoception plays a great role in creating an association between an organ and an emotion. With its help a person becomes aware of the physical changes taking place inside their body, which can be caused in particular by emotions. It was established that certain associations between organs and emotions come to exist due to “somatic bridges” while others form because of “semantic shift”. It was found that the frequency of the use of somatic expressions that express emotions was reduced in English during the industrialization and that similar changes are taking place today in Chinese. In order to explain the differences in the level of somatization it is useful to turn to the triadic structure of concepts, in accordance to which the concept “sadness” has an experiential side (an interoceptive characterization of the emotion), a notional side (a definition, verbal representation etc.) and an evaluative side. It is hypothesized that an important role of somatic expression in the expression of sadness points to the importance of the experiential side while the use of abstract words is indicative of the notional side being important. The fact that the experiential side of a concept is considered to predate the notional side explains the direction of the diachronic change from stronger somatization of emotions towards their expression with the help of more abstract notions.

Melisa Bulut, Nazmiye Yıldırım

Abstract Background Aggression is a common problem in psychiatric clinics, and many studies have been conducted on the issue over the years. This study bibliometrically examines the characteristics and trends of aggression studies in psychiatry, such as the branches, publication years, types, publishers, and impact factors, and compares the focus areas of psychiatry and psychiatric nursing on aggression, to guide future research and readers on this topic. Bibliometric analysis was performed with network metrics, citation analysis, co-authorship analysis, science mapping, and network visualization techniques using WoS analysis reports and the VOSviewer program with data obtained from the Web of Science database on January 11, 2024. Results Most studies on aggression in psychiatry were published in 2019 as English articles. Then, 3.37% of these studies are in psychiatric nursing, mainly from Australia. In contrast, most psychiatric studies are from the USA. Psychiatry studies focus on causes, types, and relationships with psychiatric disorders, while psychiatric nursing studies emphasize aggressive attitudes, risk assessment, and aggression management. Conclusions It appears that studies on aggression continue to remain current in the literature and will continue to do so, considering that aggression is an ongoing clinical problem worldwide. Despite this, studies on assessing and managing the risk of aggression are still lacking. Future studies should prioritize the management of aggression. While the study showed the presence of multidisciplinary and international collaborations, it is recommended that these collaborations be further expanded. Findings may inform researchers’ keyword and journal choices.

Advaitha Vetagiri, Gyandeep Kalita, Eisha Halder et al.

Online gender-based harassment is a widespread issue limiting the free expression and participation of women and marginalized genders in digital spaces. Detecting such abusive content can enable platforms to curb this menace. We participated in the Gendered Abuse Detection in Indic Languages shared task at ICON2023 that provided datasets of annotated Twitter posts in English, Hindi and Tamil for building classifiers to identify gendered abuse. Our team CNLP-NITS-PP developed an ensemble approach combining CNN and BiLSTM networks that can effectively model semantic and sequential patterns in textual data. The CNN captures localized features indicative of abusive language through its convolution filters applied on embedded input text. To determine context-based offensiveness, the BiLSTM analyzes this sequence for dependencies among words and phrases. Multiple variations were trained using FastText and GloVe word embeddings for each language dataset comprising over 7,600 crowdsourced annotations across labels for explicit abuse, targeted minority attacks and general offences. The validation scores showed strong performance across f1-measures, especially for English 0.84. Our experiments reveal how customizing embeddings and model hyperparameters can improve detection capability. The proposed architecture ranked 1st in the competition, proving its ability to handle real-world noisy text with code-switching. This technique has a promising scope as platforms aim to combat cyber harassment facing Indic language internet users. Our Code is at https://github.com/advaithavetagiri/CNLP-NITS-PP

José Marcos Gomes, Luis Alberto Vieira Dias

Identifying the main contributions related to the Automated Test Production (ATP) of Computer Programs and providing an overview about models, methodologies and tools used for this purpose is the aim of this Systematic Literature Review (SLR). The results will enable a comprehensive analysis and insight to evaluate their applicability. A previously produced Systematic Literature Mapping (SLM) contributed to the formulation of the ``Research Questions'' and parameters for the definition of the qualitative analysis protocol of this review.

Eileen Mitchell, Tanisha Waring, Elayne Ahern et al.

Abstract Background Homelessness is a complex societal and public health challenge. Limited information exists about the population-level health and social care-related predictors and consequences of persons with lived experience of homelessness (PEH). Studies that focus on population subgroups or ad hoc questionnaires to gather data are of relatively limited generalisability to whole-population health surveillance and planning. The aim of this study was to find and synthesise information about the risk factors for, and consequences of, experiencing homelessness in whole-population studies that used routine administrative data. Method We performed a systematic search using EMBASE, MEDLINE, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PsycINFO research databases for English-language studies published from inception until February 2023 that reported analyses of administrative data about homelessness and health and social care-related predictors and consequences. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results Of the 1224 articles reviewed, 30 publications met the inclusion criteria. The included studies examined a wide range of topic areas, and the homelessness definitions used in each varied considerably. Studies were categorised into several topic areas: Mortality, morbidity and COVID-19; health care usage and hospital re-admission; care home admission and shelter stay; and other (e.g. employment, crime victimisation). The studies reported that that the physical and mental health of people who experience homelessness was worse than that of the general population. Homeless individuals were more likely to have higher risk of hospitalisation, more likely to use emergency departments, have higher mortality rates and were at greater risk of needing intensive care or of dying from COVID-19 compared with general population. Additionally, homeless individuals were more likely to be incarcerated or unemployed. The effects were strongest for those who experienced being homeless as a child compared to those who experienced being homeless later on in life. Conclusions This is the first systematic review of whole-population observational studies that used administrative data to identify causes and consequences associated with individuals who are experiencing homelessness. While the scientific literature provides evidence on some of the possible risk factors associated with being homeless, research into this research topic has been limited and gaps still remain. There is a need for more standardised best practice approaches to understand better the causes and consequences associated with being homeless.

Alex Zarifis, Richard Ingham, Julia Kroenung

The text in the profile of those offering their properties in England in English and in Germany in German, are compared to explore whether trust is built, and privacy concerns are reduced in the same way. Six methods of building trust are used by the landlords: (1) the level of formality, (2) distance and proximity, (3) emotiveness and humor, (4) being assertive and passive aggressive, (5) conformity to the platform language style and terminology and (6) setting boundaries. Privacy concerns are not usually reduced directly as this is left to the platform. The findings indicate that language has a limited influence and the platform norms and habits are the biggest influence.

Firat Kizilirmak, Berrin Yanikoglu

We present a CNN-BiLSTM system for the problem of offline English handwriting recognition, with extensive evaluations on the public IAM dataset, including the effects of model size, data augmentation and the lexicon. Our best model achieves 3.59\% CER and 9.44\% WER using CNN-BiLSTM network with CTC layer. Test time augmentation with rotation and shear transformations applied to the input image, is proposed to increase recognition of difficult cases and found to reduce the word error rate by 2.5\% points. We also conduct an error analysis of our proposed method on IAM dataset, show hard cases of handwriting images and explore samples with erroneous labels. We provide our source code as public-domain, to foster further research to encourage scientific reproducibility.

Tetsu Kasanishi, Masaru Isonuma, Junichiro Mori et al.

Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation, the absence of large-scale datasets has been a stumbling block to the progress. We release SciReviewGen, consisting of over 10,000 literature reviews and 690,000 papers cited in the reviews. Based on the dataset, we evaluate recent transformer-based summarization models on the literature review generation task, including Fusion-in-Decoder extended for literature review generation. Human evaluation results show that some machine-generated summaries are comparable to human-written reviews, while revealing the challenges of automatic literature review generation such as hallucinations and a lack of detailed information. Our dataset and code are available at https://github.com/tetsu9923/SciReviewGen.

Abel Goedegebuure, Indika Kumara, Stefan Driessen et al.

Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks associated with centralized, monolithic data architectures in enterprises. The topic has picked the practitioners' interest, and there is considerable gray literature on it. At the same time, we observe a lack of academic attempts at defining and building upon the concept. Hence, in this article, we aim to start from the foundations and characterize the data mesh architecture regarding its design principles, architectural components, capabilities, and organizational roles. We systematically collected, analyzed, and synthesized 114 industrial gray literature articles. The review provides insights into practitioners' perspectives on the four key principles of data mesh: data as a product, domain ownership of data, self-serve data platform, and federated computational governance. Moreover, due to the comparability of data mesh and SOA (service-oriented architecture), we mapped the findings from the gray literature into the reference architectures from the SOA academic literature to create the reference architectures for describing three key dimensions of data mesh: organization of capabilities and roles, development, and runtime. Finally, we discuss open research issues in data mesh, partially based on the findings from the gray literature.

Eunice Bolanle Turawa, Tammy Charlene Hartel, André Oelofse et al.

Introduction Tobacco smoking and alcohol consumption during pregnancy are particularly prevalent in low socioeconomic status populations, with an adverse association with birth outcomes and cardiometabolic risk factors. However, the direct and indirect effects of prenatal cigarette smoking and alcohol consumption during pregnancy on cardiometabolic risk in offspring have been rather inconsistent. This may be attributed to multiple factors, such as the amount and timing of exposure to tobacco smoking and alcohol during pregnancy; the influence of maternal, environmental and socioeconomic factors; or how risk factors were defined by individual researchers and studies. Therefore, this review aims to provide a summary of the most recent evidence on birth outcomes and cardiometabolic risk in children associated with alcohol and/or tobacco exposure in utero.Methods and analysis PubMed, Scopus and Web of Science will be searched to identify published articles from 1 January 2001. Clinical studies that investigate the association between maternal cigarette smoking or alcohol consumption and birth weight and cardiometabolic risk factors in infants, children and adolescents will be included. Prospective cohort, case-control studies and birth cohort studies will be eligible for inclusion. Grey literature will be searched including conference proceedings, Google Scholar and the ProQuest Dissertation and Theses database. Only studies published in English will be included, with no restrictions regarding country, race or gender. Two independent reviewers will conduct the literature search and article screening. Eligibility criteria will be based on the population (infants, children, adolescents), exposure (maternal cigarette smoking, alcohol consumption or both), comparator (control group with no exposure during pregnancy) and outcomes (birth weight and cardiometabolic risk factors). Quality assessment and risk of bias will be assessed using a risk of bias tool for observational studies, and data will be extracted for analysis using a researcher-generated data extraction form. A meta-analysis will be performed to estimate pooled effect sizes if there are sufficient good-quality studies available. Sources of heterogeneity will be explored using subgroup analysis.Ethics and dissemination Ethical clearance will not be required as this review will extract publicly available secondary data. Findings from this review will be disseminated via publication in a peer-review journal.PROSPERO registration number CRD42021286630.

Yang Trista Cao, Anna Sotnikova, Hal Daumé et al.

NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of stereotypic group-trait associations in language models (LMs). We introduce the sensitivity test (SeT) for measuring stereotypical associations from language models. To evaluate SeT and other measures using the ABC model, we collect group-trait judgments from U.S.-based subjects to compare with English LM stereotypes. Finally, we extend this framework to measure LM stereotyping of intersectional identities.

Stefano Bannò, Marco Matassoni

The increasing demand for learning English as a second language has led to a growing interest in methods for automatically assessing spoken language proficiency. Most approaches use hand-crafted features, but their efficacy relies on their particular underlying assumptions and they risk discarding potentially salient information about proficiency. Other approaches rely on transcriptions produced by ASR systems which may not provide a faithful rendition of a learner's utterance in specific scenarios (e.g., non-native children's spontaneous speech). Furthermore, transcriptions do not yield any information about relevant aspects such as intonation, rhythm or prosody. In this paper, we investigate the use of wav2vec 2.0 for assessing overall and individual aspects of proficiency on two small datasets, one of which is publicly available. We find that this approach significantly outperforms the BERT-based baseline system trained on ASR and manual transcriptions used for comparison.