Search Results (133)

The article provides an analysis of the geographic origins of Karaites in four areas where Karaite congregations were commonly found after the Middle Ages, namely, Arabic Middle East (territories of modern Iraq, Syria, Israel, and Egypt), Constantinople/Istanbul and its area, the Crimean Peninsula, and Eastern European territories belonging today to Lithuania and Ukraine. It combines available historical, onomastic, and linguistic data revealing the migrations of Karaites to and inside these regions. For the first two regions, no ambiguity exists about the roots of local Karaites. Their ancestors were Jews who adopted the Karaite version of Judaism. For the Crimean communities, various factors favor the hypothesis about the territories of the Byzantine Empire (which later became Ottoman), and more specifically, Constantinople and its area are the only major source for their development. The Karaite communities in such historical Eastern European provinces as Lithuania (properly speaking), Volhynia, and Red Ruthenia were created after migrations from Crimea to these territories. The article also discusses medieval, cultural, and potentially genetic links between Karaites and Rabbanite Jews in the areas in question. It also addresses one phonological feature, the sibilant confusion, shared by the Galician–Volhynian dialect of the Karaim language and the Lithuanian dialect of Yiddish. Full article

Background/Objectives: This study assessed Knowledge and Practices related to Food Safety (KPFS) among nutrition services employees in hospitals across the Al-Ahsa Governorate, Kingdom of Saudi Arabia. The objective was to evaluate the staff’s understanding of key food safety principles, including foodborne illness prevention, food handling, personal hygiene, and food storage and preparation practices. Methods: A descriptive survey method was used, and data were collected using an electronic questionnaire, which was either self-administered by the participants or completed with the assistance of the researcher in cases involving employees who did not speak Arabic or English. This study included 302 staff members involved in the preparation, service, and supervision of food provided to hospital patients. Results: The results indicated a high level of knowledge among nutrition services employees regarding food safety principles, critical temperature control, cross-contamination prevention, and proper hygiene practices. The employees also demonstrated a strong commitment to personal hygiene behaviors, such as handwashing, wearing appropriate clothing, and avoiding unsafe practices. Additionally, a high degree of knowledge and understanding was found regarding food storage procedures and contamination prevention. The study also highlighted a very high level of awareness concerning the cleaning and sterilization of equipment, tools, and food storage surfaces, as well as maintaining a clean and healthy environment. These findings emphasize the importance of continuous training in enhancing food safety knowledge among nutrition services employees. Conclusions: It is recommended that all employees, regardless of education level, experience, or role, participate regularly in food safety training programs to sustain and improve food safety practices within hospital environments. Full article

Introduction: Falls pose a significant health risk to older adults, with a reported prevalence of 31.6% among Saudi older adults. Medication-related falls are a preventable cause of morbidity and mortality. This study aimed to assess fall risk, evaluate knowledge of fall-risk-increasing drugs, and examine the impact of pharmacist counseling on community-dwelling older adults in Jazan, Saudi Arabia. Methods: A cross-sectional survey was conducted from December 2023 to March 2024 among 391 community-dwelling individuals aged ≥60 years in Jazan, Saudi Arabia. Fall risk was assessed using the Arabic Stay Independent screening tool, which remains unvalidated in Arabic-speaking populations. Participants answered demographic questions and reported any pharmacist counseling on medication in the past six months. Knowledge of prescription and over-the-counter fall-risk-increasing drugs was evaluated. Multivariable logistic regression and ordered probit models were used to analyze factors associated with fall risk and drug knowledge. Results: Approximately 57% of the participants were at risk of falling. Only 11.5% demonstrated good knowledge of prescription fall-risk-increasing drugs, whereas 24.6% showed good knowledge of over-the-counter fall-risk-increasing drugs. Age (OR, 1.07; 95% CI, 1.00–1.14; p = 0.05), arthritis (OR, 5.73; 95% CI, 2.51–13.06; p < 0.001), obesity (OR, 6.00; 95% CI, 2.33–15.46; p < 0.001) and diabetes (OR, 2.79; 95% CI, 1.38–5.64; p = 0.004) were associated with increased fall risk. Those who received pharmacist counseling had a greater likelihood (95% CI, 0.020–0.167; p = 0.01) of being in the very likely category of willingness to discuss medication changes. Conclusions: The findings highlight the role of pharmacist counseling and recommend improving fall prevention through medication reviews for arthritis and diabetes patients, standardized counseling protocols, and implementation of the Stay Independent screening tool for risk assessment in older adults. Full article

This paper explores suicidality and suicide research from an autoethnographic analysis framed through grief concepts. Self-identifying as a Muslim in the United States, the author explains how lived experiences being racialized through Islamophobia, identifying as a genderfluid non-binary woman, being socially biracial, holding a postpartum bipolar diagnosis, and being connected to a diaspora, are critical elements to develop a deeper sociocultural understanding of suicide. Grief concepts that are used to analyze these themes include disenfranchised grief, ambiguous loss, anticipatory grief, and secondary loss. While these grief concepts are understood as part of the author’s embodied lived experience as an individual, there is also a collective grief that is explored through the author’s bilingual experience with Arabic as it relates to the topics of suicide and genocide occurring in the Arabic-speaking diaspora located in Gaza, Palestine. A conceptual framework is offered to make sense of the author’s lived experience by both incorporating and challenging existing academic perspectives on suicide and research. The emic, or insider, perspective is contextualized such that it may hold implications beyond the individual author, such as for U.S. Muslims and other hard-to-reach populations. A positionality statement demonstrates the author’s reflexivity of being an insider ‘participant’–researcher in conducting transformative research approaches with the U.S. Muslim community. Further directions are shared for scholars with lived experience who may seek to utilize comparable individual or collaborative autoethnographic approaches with such majority-world communities. Full article

Background: Patient-reported outcome measures (PROMs) are essential for capturing the lived experiences of individuals managing chronic diseases. However, few PROMs have been culturally adapted and validated for Arabic-speaking populations. Aim: This study aimed to translate, culturally adapt, and validate the Long-Term Conditions Questionnaire (LTCQ) for use among Arabic-speaking adults living with chronic diseases in Jordan. Methods: Following forward–backward translation and an expert review, a cross-sectional survey of 1057 adults with chronic illnesses was conducted. The psychometric evaluation involved exploratory and confirmatory factor analyses (EFA and CFA) and Rasch modelling. While the original LTCQ assumed a unidimensional structure, EFA and CFA supported a two-factor solution: Empowerment and Functional Wellbeing, and Health-Related Psychosocial Distress. Results: The Rasch analysis confirmed that the item response thresholds were ordered, with good item targeting, and no differential item functioning (DIF) by gender. The removal of one poorly performing item resulted in a refined 19-item scale with strong reliability and validity. Conclusions: The Arabic LTCQ demonstrated robust psychometric properties and cultural relevance, supporting its use in clinical care, research, and policy initiatives. Future work should examine longitudinal responsiveness and further validate the tool across diverse Arabic-speaking populations. Full article

Background/Objectives: Social participation is important for healthy aging but challenging for older immigrants because of factors such as the loss of cultural community, language and transportation barriers, ageism, and racism. This study aimed to examine (1) the type of social activities in which older immigrants from Arabic (Arab), Mandarin (East Asian), and Punjabi-speaking (South Asian) communities in Canada engage; (2) their desire for more participation in social activities; and (3) factors they perceive as preventing their engagement in more social activities. Methods: Using a cross-sectional design, we collected data, using existing measures, from 476 older immigrants between fall 2022 and winter 2023. Descriptive statistics were used to analyze the data. Results: More than 75% of participants reported engagement in three solitary activities (having a hobby, going on a day trip; and using the internet and/or email) and more than 85% participated in community-based activities with family inside and outside and with friends outside the household. Most (71%) expressed a desire to participate in more social activities in the community, but they were prevented from doing so due to factors such as language barriers or not wanting to go alone. Conclusions: Interventions are needed to facilitate community-based participation among older immigrants and improve their quality of life. Full article

The current study investigates the effect of Public Relations Campaigns in environmental education and attitudes toward environmental sustainability, and behavioral intentions among university students in the United Arab Emirates and Egypt. The questionnaire was applied to a sample of 712 male and female students from Al Ain University in the UAE and Minia University in Egypt. Theoretically supported by the theory of planned behavior (TPB), structured questionnaires were used for data gathering. Data was analyzed using partial least square–structural equation modeling (SEM), which revealed the positive effect of Public Relations Campaigns on providing environmental education and awareness among UAE Emirati and Egyptian students. Results showed a positive effect of Public Relations Campaigns on the students’ attitudes towards environmental sustainability. Finally, the effects of Public Relations Campaigns on the behavioral intention of the UAE Emirati and Egyptian students also remained positive. Overall, the results imply that Public Relations Campaigns significantly improve environmental education, shape positive attitudes toward environmental sustainability, and affect behavioral intentions among UAE and Egypt university students. These results emphasize the significance of PR initiatives in encouraging environmental awareness and promoting pro-environmental behaviors within educational settings. Also, the results highlight the effectiveness of Public Relations Campaigns in creating trust between organizations and the public while encouraging social responsibility. The study makes a dual contribution: theoretically, it expands knowledge on the relationship between public relations strategies and environmental awareness among young academics, a group influential in societal change. Practically speaking, it provides practical recommendations for educational institutions and environmental stakeholders on how to design more effective public relations campaigns to target university students and increase their level of environmental engagement. Full article

Background/Objectives: This study investigates the application of machine learning (ML) techniques in diagnosing speech sound disorders (SSDs) in Saudi Arabic-speaking children, with a specific focus on phonological biomarkers, particularly Infrequent Variance (InfrVar), to improve diagnostic accuracy. SSDs are a significant concern in pediatric speech pathology, affecting an estimated 10–15% of preschool-aged children worldwide. However, accurate diagnosis remains challenging, especially in linguistically diverse populations. Traditional diagnostic tools, such as the Percentage of Consonants Correct (PCC), often fail to capture subtle phonological variations. This study explores the potential of machine learning models to enhance diagnostic accuracy by incorporating culturally relevant phonological biomarkers like InfrVar, aiming to develop a more effective diagnostic approach for SSDs in Saudi Arabic-speaking children. Methods: Data from 235 Saudi Arabic-speaking children aged 2;6 to 5;11 years were analyzed using several machine learning models: Random Forest, Support Vector Machine (SVM), XGBoost, Logistic Regression, K-Nearest Neighbors, and Naïve Bayes. The dataset was used to classify speech patterns into four categories: Atypical, Typical Development (TD), Articulation, and Delay. Phonological features such as Phonological Variance (PhonVar), InfrVar, and Percentage of Consonants Correct (PCC) were used as key variables. SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contributions of individual features to model predictions. Results: The XGBoost and Random Forest models demonstrated the highest performance, with an accuracy of 91.49% and an AUC of 99.14%. SHAP analysis revealed that articulation patterns and phonological patterns were the most influential features for distinguishing between Atypical and TD categories. The K-Means clustering approach identified four distinct subgroups based on speech development patterns: TD (46.61%), Articulation (25.42%), Atypical (18.64%), and Delay (9.32%). Conclusions: Machine learning models, particularly XGBoost and Random Forest, effectively classified speech development categories in Saudi Arabic-speaking children. This study highlights the importance of incorporating culturally specific phonological biomarkers like InfrVar and PhonVar to improve diagnostic precision for SSDs. These findings lay the groundwork for the development of AI-assisted diagnostic tools tailored to diverse linguistic contexts, enhancing early intervention strategies in pediatric speech pathology. Full article

Visual imagery (VI) is a mental process in which an individual generates and sustains a mental image of an object without physically seeing it. Recent advancements in assistive technology have enabled the utilization of VI mental tasks as a control paradigm to design brain–computer interfaces (BCIs) capable of generating numerous control signals. This, in turn, enables the design of control systems to assist individuals with locked-in syndrome in communicating and interacting with their environment. This paper presents an electroencephalogram (EEG) dataset captured from 30 healthy native Arabic-speaking subjects (12 females and 18 males; mean age: 20.8 years; age range: 19–23) while they visually imagined the 28 letters of the Arabic alphabet. Each subject conducted 10 trials per letter, resulting in 280 trials per participant and a total of 8400 trials for the entire dataset. The EEG signals were recorded using the EMOTIV Epoc X wireless EEG headset (San Francisco, CA, USA), which is equipped with 14 data electrodes and two reference electrodes arranged according to the 10–20 international system, with a sampling rate of 256 Hz. To the best of our knowledge, this is the first EEG dataset that focuses on visually imagined Arabic letters. Full article

Background: Sensory processing difficulties can interfere with daily functioning and participation across adulthood. While standardized assessment tools exist, culturally validated instruments for Arabic-speaking adults remain limited. Objectives: This study aimed to validate the Arabic version of the Sensory Processing Measure—Second Edition (SPM-2) Adult Self-Report form in a Saudi population and evaluate its utility for the early detection of sensory processing challenges in at-risk individuals. Methods: A total of 399 Saudi adults (205 females and 194 males), aged 21 to 87 years (M = 44.1; SD = 16.2), completed the Arabic SPM-2 online. The scale consists of eight subscales, six of which form the Sensory Total score—Vision, Hearing, Touch, Taste and Smell, Body Awareness, and Balance and Motion—representing core sensory processing abilities (i.e., Sensory Total (ST)). The remaining two—Planning and Ideas and Social Participation—capture higher-order integrative functions and do not contribute to the ST. Results: The overall scale demonstrated strong internal consistency (α = 0.89), with subscale alphas ranging from 0.43 (Hearing) to 0.70 (Body Awareness). Confirmatory factor analysis (CFA) (χ² [3052] = 4147.4; p < 0.001) showed good absolute fit (RMSEA = 0.030) and moderate incremental fit (CFI = 0.74; TLI = 0.73), values that are typical for large-df models. Descriptive and cluster analyses identified distinct participant subgroups with elevated frequency ratings (scores of 2 or 3) suggestive of sensory risk. Significant age-related differences were observed across multiple sensory domains, while no significant sex-related effects were found. Conclusions: Although Social Participation and Hearing showed lower reliability, the Arabic SPM-2 exhibits sound internal structure and therefore shows promise for future clinical application once criterion validity is established. The findings support its application in culturally responsive screening, early risk identification, and intervention planning in Arabic-speaking contexts. Full article

Assessing text readability is important for helping language learners and readers select texts that match their proficiency levels. Research in cognitive psychology, which uses behavioral data such as eye-tracking and electroencephalogram signals, has shown its effectiveness in detecting cognitive activities that correlate with text difficulty during reading. However, Arabic, with its distinctive linguistic characteristics, presents unique challenges in readability assessment using cognitive data. While behavioral data have been employed in readability assessments, their full potential, particularly in Arabic contexts, remains underexplored. This paper presents the development of the first Arabic eye-tracking corpus, comprising eye movement data collected from Arabic-speaking participants, with a total of 57,617 words. Subsequently, this corpus can be utilized to evaluate a broad spectrum of text-based and gaze-based features, employing machine learning and deep learning methods to improve Arabic readability assessments by integrating cognitive data into the readability assessment process. Full article

A significant communication gap persists between the deaf and hearing communities, often leaving deaf individuals isolated and marginalised. This challenge is especially pronounced for Arabic-speaking individuals, given the lack of publicly available Arabic Sign Language datasets and dedicated recognition systems. This study is the first to use the Temporal Convolutional Network (TCN) model for Arabic Sign Language (ArSL) recognition. We created a custom dataset of the 30 most common sentences in ArSL. We improved recognition performance by enhancing a Recurrent Neural Network (RNN) incorporating a Bidirectional Long Short-Term Memory (BiLSTM) model. Our approach achieved outstanding accuracy results compared to baseline RNN-BiLSTM models. This study contributes to developing recognition systems that could bridge communication barriers for the hearing-impaired community. Through a comparative analysis, we assessed the performance of the TCN and the enhanced RNN architecture in capturing the temporal dependencies and semantic nuances unique to Arabic Sign Language. The models are trained and evaluated using the created dataset of Arabic sign gestures based on recognition accuracy, processing speed, and robustness to variations in signing styles. This research provides insights into the strengths and limitations of TCNs and the enhanced RNN-BiLSTM by investigating their applicability in sign language recognition scenarios. The results indicate that the TCN model achieved an accuracy of 99.5%, while the original RNN-BiLSTM model initially achieved a 96% accuracy but improved to 99% after enhancement. While the accuracy gap between the two models was small, the TCN model demonstrated significant advantages in terms of computational efficiency, requiring fewer resources and achieving faster inference times. These factors make TCNs more practical for real-time sign language recognition applications. Full article

Background: Pre-Trained Language Models hold significant promise for revolutionizing mental health care by delivering accessible and culturally sensitive resources. Despite this potential, their efficacy in mental health applications, particularly in the Arabic language, remains largely unexplored. To the best of our knowledge, comprehensive studies specifically evaluating the performance of PLMs on diverse Arabic mental health tasks are still scarce. This study aims to bridge this gap by evaluating the performance of pre-trained language models in classifying questions and answers within the mental health care domain. Methods: We used the MentalQA dataset, which comprises Arabic Questions and Answers interactions related to mental health. Our experiments involved four distinct learning strategies: traditional feature extraction, using PLMs as feature extractors, fine-tuning PLMs, and employing prompt-based techniques with models, such as GPT-3.5 and GPT-4 in zero-shot and few-shot learning scenarios. Arabic-specific PLMs, including AraBERT, CAMelBERT, and MARBERT, were evaluated. Results: Traditional feature-extraction methods paired with Support Vector Machines (SVM) showed competitive performance, but PLMs outperformed them due to their superior ability to capture semantic nuances. In particular, MARBERT achieved the highest performance, with Jaccard scores of 0.80 for the question classification and 0.86 for the answer classification. Further analysis revealed that fine-tuning PLMs enhances their performance, and the size of the training dataset plays a critical role in model effectiveness. Prompt-based techniques, particularly few-shot learning with GPT-3.5, demonstrated significant improvements, increasing the accuracy of question classification by 12% and the accuracy of answer classification by 45%. Conclusions: The study demonstrates the potential of PLMs and prompt-based approaches to provide mental health support to Arabic-speaking populations, providing valuable tools for individuals seeking assistance in this field. This research advances the understanding of PLMs in mental health care and emphasizes their potential to improve accessibility and effectiveness in Arabic-speaking contexts. Full article

Mental health disorders represent a substantial global health concern, impacting millions and placing a significant burden on public health systems. Natural Language Processing (NLP) has emerged as a promising tool for analyzing large textual datasets to identify and predict mental health challenges. The aim of this scoping review is to identify the Arabic NLP techniques employed in mental health research, the specific mental health conditions addressed, and the effectiveness of these techniques in detecting and predicting such conditions. This scoping review was conducted according to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) framework. Studies were included if they focused on the application of NLP techniques, addressed mental health issues (e.g., depression, anxiety, suicidal ideation) within Arabic text data, were published in peer-reviewed journals or conference proceedings, and were written in English or Arabic. The relevant literature was identified through a systematic search of four databases: PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. The results of the included studies revealed a variety of NLP techniques used to address specific mental health issues among Arabic-speaking populations. Commonly utilized techniques included Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Recurrent Neural Network (RNN), and advanced transformer-based models such as AraBERT and MARBERT. The studies predominantly focused on detecting and predicting symptoms of depression and suicidality from Arabic social media data. The effectiveness of these techniques varied, with trans-former-based models like AraBERT and MARBERT demonstrating superior performance, achieving accuracy rates of up to 99.3% and 98.3%, respectively. Traditional machine learning models and RNNs also showed promise but generally lagged in accuracy and depth of insight compared to transformer models. This scoping review highlights the significant potential of NLP techniques, particularly advanced transformer-based models, in addressing mental health issues among Arabic-speaking populations. Ongoing research is essential to keep pace with the rapidly evolving field and to validate current findings. Full article

This study examines the acoustic properties of vowels in foreigner-directed speech (FDS) in interactions between female Omani-Arabic-speaking employers and their foreign domestic helpers (FDHs). Particularly, it investigates whether Arabic corner vowels /i:/, /a:/, and /u:/ undergo acoustic adaptations in FDS. The study also explores the influence of foreign interlocutors’ psycholinguistic characteristics, such as degree of foreign accent, religion, and length of residence (LoR), on the extent of these adaptations. Data were collected from 22 Omani-Arabic-speaking women interacting with their 22 FDHs and with a native speaker (NS) confederate using a spot-the-difference task. Acoustic measures including vowel space area, formant frequency measures (F1 and F2), fundamental frequency (f0), intensity, and duration were compared across speech directed at FDHs and the NS. The results revealed that FDS exhibited greater vowel space expansion, higher F1, and increased pitch (f0) and intensity compared to speech directed at the NS confederate. However, FDS did not significantly affect F2 values. Unexpectedly, vowel duration in FDS was shorter than in speech directed at the NS. Furthermore, the psycholinguistic factors of foreign interlocutors had no significant effect on vowel space expansion in FDS. These findings provide evidence that FDS is characterized by heightened prosodic and acoustic features, potentially contributing to clearer speech. Additionally, the study highlights that NSs employ FDS when interacting with foreigners perceived to have a foreign accent. Full article