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Evidence is lacking on the impact of bilingualism on the speech skills of children with cochlear implants (CIs). This study described the speech production of children with CIs acquiring French and one or more additional spoken languages. Four groups of children aged 4-11 were included: bilinguals (n = 15) and monolinguals (n = 14) with CIs and bilinguals (n = 14) and monolinguals (n = 20) with typical hearing. Data were collected about the percentage of consonant correct (PCC) and vowel correct (PVC) produced in French and intelligibility in all languages they spoke. Bilingual and monolingual children with CIs had comparable speech accuracy in French, but the pattern differed, impacting PCC for bilinguals and PVC for monolinguals. Most children with CIs had accurate and intelligible speech in French, but few bilingual children with CIs were highly intelligible in their home language. Therefore, bilingualism did not impede the speech production outcomes of bilingual children with CIs in the language of the wider community.

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This study developed, validated, and piloted a MultiTeachViews questionnaire to investigate secondary school English as a Foreign Language (EFL) teachers' attitudes towards multilingual teaching practices such as L1 and translation use. Initially, a literature review and focus group interview with six in-service EFL teachers were conducted to capture prevailing attitudes and inform content areas for the questionnaire. Items were then crafted, followed by the adoption of a 5-point Likert scale. Validation involved assessing internal and content validity through a structured checklist and expert evaluation. The pilot phase included think-aloud protocols with two teachers and a reliability test across a broader cohort of 100 teachers. Reliability testing yielded satisfactory Cronbach's Alpha coefficients (α > .70) for all scales, affirming the instrument's internal consistency. Consequently, the instrument is found to be a reliable and valid measure of EFL teachers' attitudes towards L1 and translation use in the classroom, with significant implications for Applied Linguistic and Second Language Acquisition research.•Developed, validated, and piloted a MultiTeachViews questionnaire for investigating attitudes.•Employed mixed methods in the development, validation, and piloting phases.•Found MultiTeachViews to be a reliable and valid measure of EFL teachers' attitudes towards multilingual teaching practices, such as L1 and translation use.

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BACKGROUND: The COVID-19 pandemic underscored the challenge of swiftly disseminating research findings to a global audience. Language barriers further exacerbated disparities in access to timely scientific information, particularly for non-English speaking communities. The majority of COVID-19 research was published in English, limiting accessibility for Spanish-speaking populations. OBJECTIVE: This paper aims to assess the reach and effectiveness of AccesoCovid.com, a platform designed to disseminate up-to-date COVID-19 research in both English and Spanish, addressing the language gap in scientific communication. METHODS: AccesoCovid.com was developed through a partnership between the University of California, San Francisco (UCSF) and Universidad Nacional Autónoma de México (UNAM). The website's performance and user engagement were evaluated using Google Analytics over a span of 2 years. Key metrics included user language preference, geographical distribution, and site traffic. The website summarized and translated 1032 articles on various COVID-19 topics, such as "Pharmaceutical Interventions and Vaccines." RESULTS: From February 2021 to February 2023, the platform attracted 57,000 users. Of the 43,000 unique new visitors, 84.2% (n=36,219) hailed from Spanish-speaking regions. The majority accessed the site organically through search engines, with 88.4% (n=38,000) of users arriving this way, while 5000 (11.6%) users accessed the site directly. Most users (n=30,894, 72.1%) preferred the Spanish version of the site. The website's most accessed category was "Pharmaceutical Interventions and Vaccines," followed by "Clinical Presentation and Management" and "Mental Health." Regarding language distribution, 72.1% (n=30,894) of users primarily used Spanish; 21.4% (n=9215) used English; and 6.7% (n=2891) spoke other languages, including Portuguese, Chinese, and German. Geographically, the website attracted visitors from 179 countries, with the highest visitor counts from Mexico (n=12,342, 28.7%), Spain (n=6405, 14.9%), the United States (n=4416, 10.3%), and Peru (n=3821, 8.9%). CONCLUSIONS: AccesoCovid.com successfully bridged a critical language gap in the dissemination of COVID-19 research. Its success underscores the pressing need for multilingual scientific resources. The platform demonstrated significant user engagement and reach, particularly in Spanish-speaking countries. This highlights the potential for similar platforms to ensure equitable access to scientific knowledge across diverse linguistic communities. Future efforts should focus on expanding to other languages and conducting formal evaluations to enhance user satisfaction and impact.

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This study advances the automation of Parkinson's disease (PD) diagnosis by analyzing speech characteristics, leveraging a comprehensive approach that integrates a voting-based machine learning model. Given the growing prevalence of PD, especially among the elderly population, continuous and efficient diagnosis is of paramount importance. Conventional monitoring methods suffer from limitations related to time, cost, and accessibility, underscoring the need for the development of automated diagnostic tools. In this paper, we present a robust model for classifying speech patterns in Korean PD patients, addressing a significant research gap. Our model employs straightforward preprocessing techniques and a voting-based machine learning approach, demonstrating superior performance, particularly when training data is limited. Furthermore, we emphasize the effectiveness of the eGeMAPSv2 feature set in PD analysis and introduce new features that substantially enhance classification accuracy. The proposed model, achieving an accuracy of 84.73 % and an area under the ROC (AUC) score of 92.18 % on a dataset comprising 100 Korean PD patients and 100 healthy controls, offers a practical solution for automated diagnosis applications, such as smartphone apps. Future research endeavors will concentrate on enhancing the model's performance and delving deeper into the relationship between high-importance features and PD.

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BACKGROUND: Germany is the second most common country of immigration after the US. However, people with own or familial history of migration are not represented proportionately to the population within public health monitoring and reporting. To bridge this data gap and enable differentiated analyses on migration and health, we conducted the health interview survey GEDA Fokus among adults with Croatian, Italian, Polish, Syrian, or Turkish citizenship living throughout Germany. The aim of this paper is to evaluate the effects of recruitment efforts regarding participation and sample composition. METHODS: Data collection for this cross-sectional and multilingual survey took place between 11/2021 and 5/2022 utilizing a sequential mixed-mode design, including self-administered web- and paper-based questionnaires as well as face-to-face and telephone interviews. The gross sample (n = 33436; age range 18-79 years) was randomly drawn from the residents' registers in 120 primary sampling units based on citizenship. Outcome rates according to the American Association for Public Opinion Research, the sample composition throughout the multistage recruitment process, utilization of survey modes, and questionnaire languages are presented. RESULTS: Overall, 6038 persons participated, which corresponded to a response rate of 18.4% (range: 13.8% for Turkish citizenship to 23.9% for Syrian citizenship). Home visits accounted for the largest single increase in response. During recruitment, more female, older, as well as participants with lower levels of education and income took part in the survey. People with physical health problems and less favourable health behaviour more often took part in the survey at a later stage, while participants with symptoms of depression or anxiety more often participated early. Utilization of survey modes and questionnaire languages differed by sociodemographic and migration-related characteristics, e.g. participants aged 50 years and above more often used paper- than web-based questionnaires and those with a shorter duration of residence more often used a translated questionnaire. CONCLUSION: Multiple contact attempts, including home visits and different survey languages, as well as offering different modes of survey administration, increased response rates and most likely reduced non-response bias. In order to adequately represent and include the diversifying population in public health monitoring, national public health institutes should tailor survey designs to meet the needs of different population groups considered hard to survey to enable their survey participation.

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OBJECTIVE: To understand how Long COVID is impacting the health and social conditions of the Black and Latinx communities. BACKGROUND: Emerging research on Long COVID has identified three distinct characteristics, including multi-organ damage, persistent symptoms, and post-hospitalization complications. Given Black and Latinx communities experienced significantly higher COVID rates in the first phase of the pandemic they may be disproportionately impacted by Long COVID. METHODS: Eleven focus groups were conducted in four languages with diverse Black and Latinx individuals (n = 99) experiencing prolonged symptoms of COVID-19 or caring for family members with prolonged COVID-19 symptoms. Data was analyzed thematically. RESULTS: Most participants in non-English language groups reported they were unfamiliar with the diagnosis of long COVID, despite experiencing symptoms. Long COVID impacts spanned financial and housing stability to physical and mental health impacts. Participants reported challenging encounters with health care providers, a lack of support managing symptoms and difficulty performing activities of daily living including work. CONCLUSIONS: There is a need for multilingual, accessible information about Long COVID symptoms, improved outreach and healthcare delivery, and increased ease of enrollment in long-term disability and economic support programs.

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This article examines multilingual practices as an example of emergent pragmatic conventions in three Transient International Groups (TIGs) using spoken English as a lingua franca (ELF) from the Vienna-Oxford International Corpus of English (VOICE). The analysis combines principles of corpus linguistics and conversation analysis by adopting a new approach for the micro-diachronic analysis of spoken interaction. Quantitative and qualitative evidence and micro-diachronic visualizations of VOICE transcripts show how the three groups examined interactively develop group-specific multilingual practices. The analysis reveals that the three groups have different preferences in this respect. While two groups develop inclusive multilingual practices in the course of their interaction, one group shows a tendency to use multilingual practices exclusively, primarily in side sequences. In addition to multilingual use, the presence or absence of metalinguistic discussions about language (and languages) plays a role for creation of shared transcultural territory and the formation of group identity. These processes are indicative of how unacquainted multilingual speakers negotiate and develop pragmatic conventions more generally.

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Purpose: This case study measured how well the Lee Silverman Voice Treatment (LSVT) improved vocal features, intelligibility, and communicative effectiveness for a multilingual participant with hypokinetic-hyperkinetic dysarthria secondary to suspected progressive supranuclear palsy. LSVT treatment was chosen for the participant due to the strengths and deficits he presented with prior to treatment, and for the anticipated challenges in treatment that may arise from the presence of multilingualism and impaired cognitive functioning. Methods: A multilingual patient in their 60's (English, Spanish, and French) with hypokinetic-hyperkinetic dysarthria secondary to suspected progressive supranuclear palsy completed the standard treatment sessions for LSVT. Assessment measures were taken at baseline, immediately post-treatment, and three-months post-treatment. Results: Improvements were measured in vocal quality, vocal loudness, intelligibility, and communicative effectiveness immediately post-treatment. Three months post-treatment, improvements in vocal quality and intelligibility were maintained. Conclusion: This case study illustrates that LSVT may be a beneficial treatment for complex clients who are multilingual and present with complex comorbidities and cognitive deficits. LSVT resulted in some meaningful changes in vocal quality, intelligibility, and communicative effectiveness for this individual. Clinicians who work with complex patients may wish to consider the theoretical underpinnings of LSVT, client profile, areas of client need, and ability and desire to complete an intensive treatment program to determine if trialing LSVT is appropriate. The use of LSVT with complex clients may yield positive outcomes.

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The Calgary-Cambridge Guide is a widely recognised framework for teaching communication skills to healthcare professionals that has become a cornerstone of communication training programs in medicine and other healthcare fields. In the context of veterinary medicine, its integration into communication training programs has become an asset improving communication, education, interaction, and quality of service, enhancing the veterinary-client-patient relationship (VCPR). In veterinary medicine, however, a more challenging consultation dynamic involves the veterinarian, the owner, and the animal. The addition of a veterinary assistant that acts as an interpreter or translator is common in Hong Kong where the native language (Cantonese) coexists with English when consultations are led by non-native language speakers. This addition converts this commonly dyadic model into a triadic communication model. The addition of an assistant interpreter influences the way consultations are conducted, how information is conveyed, and how interpersonal cues and empathy are delivered. In this report we depict challenges applying the Calgary-Cambridge Guide in multicultural and multilingual veterinary medical centres in Hong Kong and highlight the role of veterinary supporting staff in these scenarios, specifically veterinary assistant interpreters.

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Sentiment analysis in the public security domain involves analysing public sentiment, emotions, opinions, and attitudes toward events, phenomena, and crises. However, the complexity of sarcasm, which tends to alter the intended meaning, combined with the use of bilingual code-mixed content, hampers sentiment analysis systems. Currently, limited datasets are available that focus on these issues. This paper introduces a comprehensive dataset constructed through a systematic data acquisition and annotation process. The acquisition process includes collecting data from social media platforms, starting with keyword searching, querying, and scraping, resulting in an acquired dataset. The subsequent annotation process involves refining and labelling, starting with data merging, selection, and annotation, ending in an annotated dataset. Three expert annotators from different fields were appointed for the labelling tasks, which produced determinations of sentiment and sarcasm in the content. Additionally, an annotator specialized in literature was appointed for language identification of each content. This dataset represents a valuable contribution to the field of natural language processing and machine learning, especially within the public security domain and for multilingual countries in Southeast Asia.

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Many parents express concerns for their children's multilingual development, yet little is known about the nature and strength of these concerns - especially among parents in multilingual societies. This pre-registered, questionnaire-based study addresses this gap by examining the concerns of 821 Quebec-based parents raising infants and toddlers aged 0-4 years with multiple languages in the home. Factor analysis of parents' Likert-scale responses revealed that parents had (1) concerns regarding the effect of children's multilingualism on their cognition, and (2) concerns regarding children's exposure to and attainment of fluency in their languages. Concern strength was moderate to weak, and cognition concerns were weaker than exposure-fluency concerns. Transmission of a heritage language, transmission of three or more languages, presence of developmental issues, and less positive parental attitudes towards childhood multilingualism were associated with stronger concerns. These findings have both theoretical and practical implications: they advance our understanding of parental concerns and facilitate the development of support for multilingual families.

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The incidences of mental health illnesses, such as suicidal ideation and depression, are increasing, which highlights the urgent need for early detection methods. There is a growing interest in using natural language processing (NLP) models to analyze textual data from patients, but accessing patients' data for research purposes can be challenging due to privacy concerns. Federated learning (FL) is a promising approach that can balance the need for centralized learning with data ownership sensitivity. In this study, we examine the effectiveness of FL models in detecting depression by using a simulated multilingual dataset. We analyzed social media posts in five different languages with varying sample sizes. Our findings indicate that FL achieves strong performance in most cases while maintaining clients' privacy for both independent and non-independent client partitioning.

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The ADReSS-M Signal Processing Grand Challenge was held at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The challenge targeted difficult automatic prediction problems of great societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD) and the estimation of cognitive test scoress. Participants were invited to create models for the assessment of cognitive function based on spontaneous speech data. Most of these models employed signal processing and machine learning methods. The ADReSS-M challenge was designed to assess the extent to which predictive models built based on speech in one language generalise to another language. The language data compiled and made available for ADReSS-M comprised English, for model training, and Greek, for model testing and validation. To the best of our knowledge no previous shared research task investigated acoustic features of the speech signal or linguistic characteristics in the context of multilingual AD detection. This paper describes the context of the ADReSS-M challenge, its data sets, its predictive tasks, the evaluation methodology we employed, our baseline models and results, and the top five submissions. The paper concludes with a summary discussion of the ADReSS-M results, and our critical assessment of the future outlook in this field.

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There have been many published picture corpora. However, more than half of the world's population speaks more than one language and, as language and culture are intertwined, some of the items from a picture corpus designed for a given language in a particular culture may not fit another culture (with the same or different language). There is also an awareness that language research can gain from the study of bi-/multilingual individuals who are immersed in multilingual contexts that foster inter-language interactions. Consequently, we developed a relatively large corpus of pictures (663 nouns, 96 verbs) and collected normative data from multilingual speakers of Kannada (a southern Indian language) on two picture-related measures (name agreement, image agreement) and three word-related measures (familiarity, subjective frequency, age of acquisition), and report objective visual complexity and syllable count of the words. Naming labels were classified into words from the target language (i.e., Kannada), cognates (borrowed from/shared with another language), translation equivalents, and elaborations. The picture corpus had > 85% mean concept agreement with multiple acceptable names (1-7 naming labels) for each concept. The mean percentage name agreement for the modal name was > 70%, with H-statistics of 0.89 for nouns and 0.52 for verbs. We also analyse the variability of responses highlighting the influence of bi-/multilingualism on (picture) naming. The picture corpus is freely accessible to researchers and clinicians. It may be used for future standardization with other languages of similar cultural contexts, and relevant items can be used in languages from different cultures, following suitable standardization.

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OBJECTIVE: Efforts to understand the global variability in cognitive profiles in patients with epilepsy have been stymied by the lack of a standardized diagnostic system. This study examined the cross-cultural applicability of the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) in a cohort of patients with temporal lobe epilepsy (TLE) in India that was diverse in language, education, and cultural background. METHODS: A cohort of 548 adults with TLE from Mumbai completed a presurgical comprehensive neuropsychological evaluation. The IC-CoDE taxonomy was applied to derive cognitive phenotypes in the sample. Analyses of variance were conducted to examine differences in demographic and clinical characteristics across the phenotypes, and chi-squared tests were used to determine whether the phenotype distribution differed between the Mumbai sample and published data from a multicenter US sample. RESULTS: Using the IC-CoDE criteria, 47% of our cohort showed an intact cognitive profile, 31% a single-domain impairment, 16% a bidomain impairment, and 6% a generalized impairment profile. The distribution of cognitive phenotypes was similar between the Indian and US cohorts for the intact and bidomain phenotypes, but differed for the single and generalized domains. There was a larger proportion of patients with single-domain impairment in the Indian cohort and a larger proportion with generalized impairment in the US cohort. Among patients with single-domain impairment, a greater proportion exhibited memory impairment in the Indian cohort, whereas a greater proportion showed language impairment in the US sample, likely reflecting differences in language administration procedures and sample characteristics including a higher rate of mesial temporal sclerosis in the Indian sample. SIGNIFICANCE: Our results demonstrate the applicability of IC-CoDE in a group of culturally and linguistically diverse patients from India. This approach enhances our understanding of cognitive variability across cultures and enables harmonized and inclusive research into the neuropsychological aspects of epilepsy.

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BACKGROUND: Physical inactivity is associated with adverse health outcomes among Asian Americans, who exhibit the least adherence to physical activity guidelines compared with other racial and ethnic groups. Mobile app-based interventions are a promising approach to promote healthy behaviors. However, there is a lack of app-based interventions focused on improving physical activity among Asian Americans whose primary language is not English. OBJECTIVE: This pilot study aimed to assess the feasibility and acceptability of a 5-week intervention using a culturally and linguistically adapted, evidence-based mobile phone app with an accelerometer program, to promote physical activity among Chinese-, Tagalog-, or Vietnamese-speaking Americans. METHODS: Participants were recruited through collaborations with community-based organizations. The intervention was adapted from a 12-month physical activity randomized controlled trial involving the app and accelerometer for English-speaking adults. Sociodemographic characteristics, lifestyle factors, and physical measurements were collected at the baseline visit. A 7-day run-in period was conducted to screen for the participants who could wear a Fitbit One (Fitbit LLC) accelerometer and complete the app's daily step diary. During the 4-week intervention period, participants wore the accelerometer and reported their daily steps in the app. Participants also received daily messages to reinforce key contents taught during an in-person educational session, remind them to input steps, and provide tailored feedback. Feasibility measures were the percentage of eligible participants completing the run-in period and the percentage of participants who used the app diary for at least 5 out of 7 days during the intervention period. We conducted poststudy participant interviews to explore overall intervention acceptability. RESULTS: A total of 19 participants were enrolled at the beginning of the study with a mean age of 47 (SD 13.3; range 29-70) years, and 58% (n=11) of them were female. Of the participants, 26% (n=5) were Chinese, 32% (n=6) were Vietnamese, and 42% (n=8) were Filipino. All participants met the run-in criteria to proceed with the intervention. Adherence to the app diary ranged from 74% (n=14) in week 2 to 95% (n=18) in week 4. The daily average steps per week from accelerometers increased each week from 8451 (SD 3378) steps during the run-in period to 10,930 (SD 4213) steps in week 4. Participants reported positive experiences including an increased motivation to walk and the enjoyment of being able to monitor their physical activity. CONCLUSIONS: This is the first pilot study of a multicomponent intervention and evidence-based mobile phone app to promote physical activity among Asian Americans who use apps in traditional Chinese, Tagalog, or Vietnamese, which demonstrated high feasibility and acceptability. Future work focused on multilingual mobile apps to address disparities in physical inactivity among Asian Americans should be considered.

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In this paper, the dynamic behaviors and control strategies of a rumor propagation model are studied in multi-lingual environment. First, an S2E2I2R rumor propagation model is proposed, which incorporates a non-smooth inhibition mechanism. Meanwhile, the existence and stability of the equilibrium are analyzed, grounded in the spreader threshold of the government intervention. Finally, the optimal control and the event-triggered impulsive control strategies are proposed to mitigate the spread of rumors, and the comparison of their effectiveness is further presented by the numerical simulation and a practical case.

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Traditional monolingual word embedding models transform words into high-dimensional vectors which represent semantics relations between words as relationships between vectors in the high-dimensional space. They serve as productive tools to interpret multifarious aspects of the social world in social science research. Building on the previous research which interprets multifaceted meanings of words by projecting them onto word-level dimensions defined by differences between antonyms, we extend the architecture of establishing word-level cultural dimensions to the sentence level and adopt a Language-agnostic BERT model (LaBSE) to detect position similarities in a multi-language environment. We assess the efficacy of our sentence-level methodology using Twitter data from US politicians, comparing it to the traditional word-level embedding model. We also adopt Latent Dirichlet Allocation (LDA) to investigate detailed topics in these tweets and interpret politicians' positions from different angles. In addition, we adopt Twitter data from Spanish politicians and visualize their positions in a multi-language space to analyze position similarities across countries. The results show that our sentence-level methodology outperform traditional word-level model. We also demonstrate that our methodology is effective dealing with fine-sorted themes from the result that political positions towards different topics vary even within the same politicians. Through verification using American and Spanish political datasets, we find that the positioning of American and Spanish politicians on our defined liberal-conservative axis aligns with social common sense, political news, and previous research. Our architecture improves the standard word-level methodology and can be considered as a useful architecture for sentence-level applications in the future.

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To obtain a reliable and accurate automatic speech recognition (ASR) machine learning model, it is necessary to have sufficient audio data transcribed, for training. Many languages in the world, especially the agglutinative languages of the Turkic family, suffer from a lack of this type of data. Many studies have been conducted in order to obtain better models for low-resource languages, using different approaches. The most popular approaches include multilingual training and transfer learning. In this study, we combined five agglutinative languages from the Turkic family-Kazakh, Bashkir, Kyrgyz, Sakha, and Tatar,-in order to provide multilingual training using connectionist temporal classification and an attention mechanism including a language model, because these languages have cognate words, sentence formation rules, and alphabet (Cyrillic). Data from the open-source database Common voice was used for the study, to make the experiments reproducible. The results of the experiments showed that multilingual training could improve ASR performances for all languages included in the experiment, except Bashkir language. A dramatic result was achieved for the Kyrgyz language: word error rate decreased to nearly one-fifth and character error rate decreased to one-fourth, which proves that this approach can be helpful for critically low-resource languages.

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INTRODUCTION: Patients use the internet to learn more about health conditions. Non-English-speaking patients may face additional challenges. The quality of online breast cancer information, the most common cancer in women, is uncertain. This study aims to examine the quality of online breast cancer information for English and non-English-speaking patients. METHODS: Three search engines were queried using the terms: "how to do a breast examination," "when do I need a mammogram," and "what are the treatment options for breast cancer" in English, Spanish, and Chinese. For each language, 60 unique websites were included and classified by type and information source. Two language-fluent reviewers evaluated website quality using the Journal of American Medical Association benchmark criteria (0-4) and the DISCERN tool (1-5), with higher scores representing higher quality. Scores were averaged for each language. Health On the Net code presence was noted. Inter-rater reliability between reviewers was assessed. RESULTS: English and Spanish websites most commonly originated from US sources (92% and 80%, respectively) compared to Chinese websites (33%, P < 0.001). The most common website type was hospital-affiliated for English (43%) and foundation/advocacy for Spanish and Chinese (43% and 45%, respectively). English websites had the highest and Chinese websites the lowest mean the Journal of American Medical Association (2.2 ± 1.4 versus 1.0 ± 0.8, P = 0.002) and DISCERN scores (3.5 ± 0.9 versus 2.3 ± 0.6, P < 0.001). Health On the Net code was present on 16 (8.9%) websites. Inter-rater reliability ranged from moderate to substantial agreement. CONCLUSIONS: The quality of online information on breast cancer across all three languages is poor. Information quality was poorest for Chinese websites. Improvements to enhance the reliability of breast cancer information across languages are needed.

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