Search Results (74)

Introduction: Racial and ethnic disparities in end-of-life (EOL) cancer care persist, leading to lower rates of advance care planning (ACP), reduced access to palliative care, and poorer patient outcomes for minority populations. While previous research has documented these inequities, less is known about the specific interventions developed to address them, necessitating a comprehensive review of existing strategies aimed at improving EOL care for racial and ethnic populations. The objective of this scoping review is to examine the extent and characteristics of interventions and their outcomes designed to address racial and ethnic disparities in EOL cancer care in the United States. Methods: A comprehensive search of EOL cancer care interventions for minority populations was conducted in Ovid MEDLINE, CINAHL with Full Text (EBSCOhost), and Scopus (Elsevier) in September 2024. Two independent reviewers screened titles, abstracts, and full texts following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, with inclusion limited to studies conducted in the US and published in English. Results: Of 3104 screened studies, 10 met the inclusion criteria. Participants enrolled were only from Latino (n = 6 studies) or Black (n = 4 studies) populations. We identified four types of interventions, including communication skills for patients, caregivers, researchers, and clinicians (n = 2), education programs for patients (n = 1), navigation and support programs for patients and caregivers (n = 3), and training programs for health workers and community leaders (n = 4). The most effective interventions were those that addressed linguistic barriers, integrated cultural values, and involved trusted community figures. Faith-based models were particularly successful among African American patients, while bilingual navigation and family-centered ACP interventions had the greatest impact in Latino populations. Conclusions: This review highlights (1) the importance of culturally tailored interventions for specific minority populations and (2) the limited number of such interventions, which primarily target only the largest minority groups. Full article

This paper presents a comprehensive synchronic and diachronic analysis of the Sino-Vietnamese negative prefixes bất (Chinese 不 bù), vô (無 wú), and phi (非 fēi), examining their historical development and modern usage in Vietnamese, with a comparative perspective on their Chinese equivalents. By investigating the interaction between these prefixes and Vietnamese sentential negators—such as the native chẳng and the Chinese-derived không—the study explores the evolution of negation in Vietnamese over several centuries. The research draws on a corpus of three bilingual Classical Chinese–Vietnamese translations of Confucius’s Analects from the 17th, 19th, and 21st centuries, two written in traditional Nôm script and one in the modern Quốc ngữ alphabet. This corpus provides valuable insights into linguistic shifts driven by language contact in Vietnam. The findings reveal that in the 17th century, the Sino-Vietnamese prefixes bất, vô, and phi were largely absent, with native chẳng dominating. By the 19th century, chẳng persisted, but không emerged as a sentential negator, and bất appeared, both reflecting Chinese forms and demonstrating innovative uses. In the 21st century, không became the dominant negator, with bất and vô seeing increased usage, reflecting broader trends of linguistic modernization. This study situates these changes within the broader context of 20th-century East Asian literacy expansion, where Japan played a pivotal role in disseminating modernized Chinese-based vocabulary. By examining the selective adaptation and integration of Sino-Vietnamese elements, this paper contributes to a deeper understanding of language contact, syntactic influence, and lexical innovation in the evolving Vietnamese lexicon. Full article

The COVID-19 pandemic necessitated an urgent shift to emergency remote learning, significantly affecting the education of bilingual students. This study examines the use of technology-enhanced drama-based methods, specifically digital Readers Theater, as an emergency measure to enhance reading literacy and psychosocial support during the pandemic amid multiple concurrent crises. Using an action research approach, 37 Greek teachers implemented remote literacy activities involving digital drama in their virtual classrooms over a period of four weeks. Data were collected from semi-structured interviews, teachers’ reflective journals, and student participation and were analyzed through Ajzen’s Theory of Planned Behavior. The findings indicate that, despite technological challenges and adaptation difficulties, the creative use of digital drama activities contributed to improvements in students’ reading skills, motivation, and engagement. The intervention also fostered students’ emotional expression and social connection, contributing to their psychosocial support during the disruption. This study highlights the dual role of technology as both an enabler and a barrier, emphasizing the need for better digital infrastructure and comprehensive teacher training for effective emergency response. The results stress the value of technology-supported, arts-based approaches in maintaining student engagement and promoting bilingual literacy in crisis contexts. These findings provide useful guidance for teachers and school leaders on how to support students’ learning and well-being, both during crises and in everyday practice. Full article

Paraphrasing means expressing the semantic meaning of a text using different words. Paraphrasing has a significant impact on numerous Natural Language Processing (NLP) applications, such as Machine Translation (MT) and Question Answering (QA). Machine Learning (ML) methods are frequently employed to generate new paraphrased text, and the generative method is commonly used for text generation. Generative Pre-trained Transformer (GPT) models have demonstrated effectiveness in various text generation tasks, including summarization, proofreading, and rephrasing of English texts. However, GPT-4’s capabilities in Arabic paraphrase generation have not been extensively studied despite Arabic being one of the most widely spoken languages. In this paper, the researchers evaluate the capabilities of GPT-4 in text paraphrasing for Arabic. Furthermore, the paper presents a comprehensive evaluation method for paraphrase quality and developing a detailed framework for evaluation. The framework comprises Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Lexical Diversity (LD), Jaccard similarity, and word embedding using the Arabic Bi-directional Encoder Representation from Transformers (AraBERT) model with cosine and Euclidean similarity. This paper illustrates that GPT-4 can effectively produce a new paraphrased sentence that is semantically equivalent to the original sentence, and the quality framework efficiently ranks paraphrased pairs according to quality criteria. Full article

Children around the world often grow up with multiple language varieties and are exposed to regional and second-language accents. This linguistic heterogeneity presents both benefits and challenges for cognitive and language development. Recognizing the importance of input variability in theories of language processing, researchers are now using more nuanced assessments of language experience that go beyond simple ‘monolingual’ versus ‘bilingual’ categories. These assessment methods capture the gradient nature of language exposure and use. This article provides a narrative review of recent research on the role of different accents and languages in children’s environments. It emphasizes the importance of applying gradient assessments of accent variation to both bilingual and monolingual populations. In doing so, a more comprehensive understanding of speech processing in heterogeneous contexts among school-aged children can be achieved. Full article

The timely identification of probable causes in aviation incidents is crucial for averting future tragedies and safeguarding passengers. Typically, investigators rely on flight data recorders; however, delays in data retrieval or damage to the devices can impede progress. In such instances, experts resort to supplementary sources like eyewitness testimonies and radar data to construct analytical narratives. Delays in this process have tangible consequences, as evidenced by the Boeing 737 MAX accidents involving Lion Air and Ethiopian Airlines, where the same design flaw resulted in catastrophic outcomes. To streamline investigations, scholars advocate for natural language processing (NLP) and topic modelling methodologies, which organize pertinent aviation terms for rapid analysis. However, existing techniques lack a direct mechanism for deducing probable causes. To bridge this gap, this study trains and evaluates the performance of a transformer-based model in predicting the likely causes of aviation incidents based on long-input raw text analysis narratives. Unlike traditional models that classify incidents into predefined categories such as human error, weather conditions, or maintenance issues, the trained model infers and generates the likely cause in a human-like narrative, providing a more interpretable and contextually rich explanation. By training the model on comprehensive aviation incident investigation reports like those from the National Transportation Safety Board (NTSB), the proposed approach exhibits promising performance across key evaluation metrics, including BERTScore with Precision: (M = 0.749, SD = 0.109), Recall: (M = 0.772, SD = 0.101), F1-score: (M = 0.758, SD = 0.097), Bilingual Evaluation Understudy (BLEU) with (M = 0.727, SD = 0.33), Latent Semantic Analysis (LSA similarity) with (M = 0.696, SD = 0.152), and Recall Oriented Understudy for Gisting Evaluation (ROUGE) with a precision, recall and F-measure scores of (M = 0.666, SD = 0.217), (M = 0.610, SD = 0.211), (M = 0.618, SD = 0.192) for rouge-1, (M = 0.488, SD = 0.264), (M = 0.448, SD = 0.257), M = 0.452, SD = 0.248) for rouge-2 and (M = 0.602, SD = 0.241), (M = 0.553, SD = 0.235), (M = 0.5560, SD = 0.220) for rouge-L, respectively. This demonstrates its potential to expedite investigations by promptly identifying probable causes from analysis narratives, thus bolstering aviation safety protocols. Full article

This study explored how bilingual Latine teacher candidates (TCs)—undergraduate students in a teacher preparation program working toward obtaining a teaching license and identifying as individuals from Latin America or of Latin American descent, using the gender-neutral term in Spanish, “Latine”, to encompass all genders—identified and addressed cultural assumptions in mathematics questions on the STAAR (State of Texas Assessments of Academic Readiness) test. Twenty Latine TCs who were enrolled at a major southern research university (pseudonym: Southland University) program reviewed fifth-grade STAAR mathematics questions to assess cultural assumptions and suggest revisions for cultural relevancy. The findings reveal that the TCs identified cultural assumptions in questions about probable unfamiliar currency, non-standard measurement units, and culturally specific terms that could impede students’ understanding. In their revisions, the TCs proposed simplifying language and provided contextual examples to enhance clarity, aligning with the first tenet of culturally relevant pedagogy (CRP). However, few revisions addressed CRP’s second and third tenets, which involve fostering cultural competence and critical consciousness. This study underscores the importance of integrating comprehensive CRP training in teacher preparation programs to better equip TCs to create culturally responsive teaching practices. The findings contribute to ongoing discussions about improving the cultural relevancy in standardized tests and supporting diverse student populations in achieving academic success. Full article

Carbon neutrality is a critical global objective in the fight against climate change. As relevant knowledge and technologies advance rapidly, there is an escalating demand for sophisticated intelligent services. While large language models (LLMs) have demonstrated considerable promise in knowledge processing and generation, their application within the domain of carbon neutrality remains in the early stages of exploration. This study develops a carbon neutrality knowledge base (CN Knowledge Base) using the ChatGLM3 model aimed at enhancing question-answering capabilities in areas such as carbon emission monitoring, policy interpretation, and technical optimization. By refining domain-specific corpora and integrating a Retrieval-Augmented Generation (RAG) mechanism, the model’s ability to generate accurate and relevant responses is improved. To evaluate the performance of the proposed system, a comprehensive quantitative comparison is conducted using established evaluation metrics, including BLEU (Bilingual Evaluation Understudy), BERT (Bidirectional Encoder Representations from Transformers), and METEOR (Metric for Evaluation of Translation with Explicit Ordering). The CN Knowledge Base is benchmarked against leading models such as GPT-4, Gemini, and Bing. The results demonstrate that the CN Knowledge Base outperforms other models in METEOR (0.2697) and is comparable to GPT-4o in both BLEU (0.8755) and BERT (0.8260) Scores (GPT-4o: BLEU: 0.8784, BERT: 0.8404). These findings underscore the knowledge base’s strong adaptability and its ability to generate high-quality, domain-specific content. The study suggests that specialized models can overcome the limitations of general-purpose LLMs, particularly in precise terminology and accurate application of domain knowledge. With continued development, such models could significantly enhance digital and intelligent solutions for carbon neutrality and related fields. Full article

The complexities of refugee resettlement in the U.S. require comprehensive mental health and psychosocial support (MHPSS) strategies, yet significant gaps persist due to resettlement policies prioritizing short-term self-sufficiency over long-term mental health and well-being. This study explores the shift from traditional “burden sharing” to “task sharing” models that emphasize community-initiated care (CIC). Using a two-phase qualitative method, 27 refugee leaders and bilingual service providers from 14 refugee communities, including Afghan, Bhutanese, Burmese, Congolese, Somali, and Sudanese communities, across four states participated in focus groups or key informant interviews. Thematic analysis revealed that peer support models play a critical role as bridges between cultures, service sectors, and formal and informal networks. However, peer support remains insufficient without structural reform, as refugees face barriers such as lack of professional development and power imbalances with professionals while managing their own life challenges. This study emphasizes formalizing CIC models that empower refugees to lead the sharing process in collaborative care. Intersectoral collaboration and supportive policy frameworks are necessary to sustain peer support and build long-term leadership capacity. The findings highlight the need for a system-level shift to ensure equitable responsibility for care, fostering sustainable, community-driven MHPSS solutions in refugee resettlement. Full article

In the present study, we set out to examine the effect of background noise on bilingual language comprehension between a person’s first language (L1) and second language (L2). Language control systems seem to systematically engage in bilingual language production, as evidenced by the presence of switch costs with slower responses to switch trials than repeat trials. However, this systematic engagement does not uniformly apply to comprehension, as the involvement of language control in bilingual comprehension may vary depending on external contexts. In two experiments, we investigated how background noise influenced language switching in comprehension for unbalanced Chinese–English bilinguals. Overall, when comprehending words from two languages, participants experienced significant language switch costs across all conditions, but smaller switch costs were observed in the noise condition than in the quiet condition. However, the symmetrical patterns of switch costs were not modulated by background noise. This is the first study that supports the flexibility of bilingual language comprehension depending on the presence of background noise, expanding the adaptive control hypothesis. Full article

This mixed-methods study investigates the impact of digital devices use at home on early English literacy development among 121 Chinese-Canadian first graders, as well as their parents’ perspectives on the families’ purposes and practices of digital devices use for English literacy, highlighting bilingual children’s experiences with multiple digital devices. To understand the relationships between the children’s digital practices and early English literacy development, we collected quantitative data on children’s English literacy skills—including reading comprehension, decoding, and receptive vocabulary—alongside frequency data on digital devices use including those on computer use and television viewing. Parents of 66 children participated in interviews, providing context-specific insights into devices use purposes and language learning practices. Data analysis included descriptive statistics and independent samples t-tests to examine group differences. Hierarchical linear regression explored associations between digital devices use and early English literacy outcomes, while thematic analysis identified patterns from parental interviews. Results indicate a positive association between controlled television exposure and receptive vocabulary. However, no significant relationship was observed between television or computer use and decoding or reading comprehension. Findings emphasize the multifaceted but limited role of digital devices, with parents struggling to balance educational benefits and long-term concerns. Full article

Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has sought to bolster intelligent processing capabilities with regard to Latin Cuengh through data augmentation techniques leveraging scarce textual data, with modest success. In this study, we introduce an innovative multimodal seed data augmentation model designed to significantly enhance the intelligent recognition and comprehension of this dialect. After supplementing the pre-trained model with extensive speech data, we fine-tune its performance with a modest corpus of multilingual textual seed data, employing both Latin Cuengh and Chinese texts as bilingual seed data to enrich its multilingual properties. We then refine its parameters through a variety of downstream tasks. The proposed model achieves a commendable performance across both multi-classification and binary classification tasks, with its average accuracy and F1 measure increasing by more than 3%. Moreover, the model’s training efficiency is substantially ameliorated through strategic seed data augmentation. Our research provides insights into the informatization of low-resource languages and contributes to their dissemination and preservation. Full article

Over the past few years, several reforms in language education have been outlined to implement the full-scale bilingualization of the education system in Taiwan. Among the 3392 public primary and secondary schools, nearly one-third of them have been implementing bilingual education in certain subjects. The vast majority of these schools have adopted Immersion, CLIL, or EMI as models for bilingual education. It has been argued that these exotic models cannot be “transplanted” to the education context of Taiwan, and he proposes the FERTILE model. Adopting this model as the theoretical framework, this qualitative study aims to examine how school practitioners plan and practice bilingual education in Taiwan. Data were collected through the document analysis of the progress reports from thirty-five primary and secondary schools and three focus group discussions (FGDs) with nine subject teachers. The findings show that the participants made considerable efforts to construct a bilingual environment and have an identical understanding about the strategies for bilingual instruction that emphasize subject knowledge and students’ comprehension. However, the participants experienced a diverse ecology of bilingual teacher learning communities and received differential support from the principals. Qualified and stable teaching manpower was identified as the key to sustain bilingual education. Implications to different education stakeholders are also discussed. Full article

Introduction: The objective of the current study was two-fold. First, it aimed to estimate receptive vocabulary and expressive syntax skills in L2 Italian among early sequential/simultaneous bilingual children of migrant single-mother families with very low socioeconomic status (SES). This objective was achieved by matching the participants’ performance with normative data. Secondly, this study aimed to identify which individual and language exposure factors contributed to learning L2 vocabulary and syntax. Methods: Twenty-four early sequential/simultaneous bilingual children (age range = 5.10–12.4 years) and their mothers were enrolled. Mothers answered questions about linguistic biography and demographic information. Children completed Lexical Comprehension, Sentence Repetition, and Non-Word Repetition tasks from the Language Assessment Battery for 4–12-year-olds to, respectively, assess receptive vocabulary, expressive syntax, and phonological processing. Moreover, non-verbal intellectual functioning was evaluated by the Raven’s Test. Results/Discussion: Compared to normative data, 20 children showed lower receptive vocabulary abilities (<−1.5 SD), 24 lower expressive syntax skills (−2DS), and 7 children lower phonological processing (<−1.5 DS). Moreover, L2 phonological processing and the length of L2 exposure in an educational context positively predicted L2 receptive vocabulary as well as L2 expressive syntax skills. To date, performance in L2 among early sequential/simultaneous bilingual children from migrant households and very low SES remains underexplored. Future efforts need to be directed towards the understanding of factors that impact oral competence in L2, considering that these children will also be exposed to written L2 in the school context. Full article

The expansion of English medium instruction (EMI) in higher education has generated significant scholarly interest, resulting in an increasing body of research across different contexts. This bibliometric study examines 1522 publications in the Scopus database to explore the intellectual, conceptual, and social structure of the EMI literature in higher education. Findings revealed substantial growth in publications and citations between 1974 and 2024, showing a notable increase in productivity after 2018. Most cited authors focus on EMI within their affiliated country, but some affiliated with British universities have made global contributions. The field exhibits global coverage, albeit with strong dominance by China, Spain, the UK, Australia, and Hong Kong, as well as limited representation from African nations, barring South Africa. EMI networks are primarily driven by authors’ current and past institutional affiliations as well as geographical proximity, with the UK, Spain, and China emerging as leaders in these networks. The most productive journals focus on multilingualism, bilingualism, language policy, teaching, and learning while also encompassing higher education and multidisciplinary areas. Key topics signal a shift towards translanguaging and classroom interaction. Under-researched areas include (post)colonialism and EMI implementation. These findings provide a comprehensive insight into the evolving landscape of EMI research and potential future directions. Full article