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It Takes a Village: Unpacking Contextual Factors Influencing Caregiving in Urban Poor Neighbourhoods of Bangalore, South India
No full textBackground: Caregivers in urban settings often face unique challenges in providing nurturing care. This qualitative study explores the complex realities of caregiving among mothers and grandmothers in urban poor neighbourhoods of Bangalore, South India. Grounded in Bronfenbrenner’s ecological systems theory, this is the first study in urban India that examines how caregivers’ perceptions, along with individual and systemic factors, shape caregiving practices in this setting. Methods: In-depth interviews (IDIs) were conducted with 22 mothers and grandmothers of 4–6-year-old children from the urban MAASTHI cohort in Bangalore, South India. Topic guides were developed, pre-tested, and piloted. IDIs were conducted in local languages (Kannada and Hindi). Transcripts were coded using NVivo 12 plus and analyzed via a thematic analysis approach using Bronfenbrenner’s ecological systems framework to organize themes. Findings: At the microsystem level, caregivers engaged with children through storytelling and play, though competing demands like household chores often constrained these interactions. Disciplining techniques varied, and the absence of fathers placed additional burdens on mothers. The mesosystem revealed the critical role of extended family in providing support. At the exosystem level, unsafe neighbourhoods limited children’s opportunities for outdoor play. The macrosystem highlighted how religious values provided moral frameworks for parenting and the presence of stigma against single mothers. The chronosystem explored declining social support over time and challenges. Conclusions: These findings emphasize that caregiving inequities are not isolated but structurally embedded, demanding interventions that address sociocultural, economic, and spatial barriers to equitable support for caregivers, particularly those in disadvantaged settings. It calls for context-sensitive interventions, including community-based parenting programmes including maternal well-being, strengthening community and public support systems, improving safe play spaces, and longitudinal research. By amplifying marginalized caregivers’ voices, this research highlights the need for policies that support nurturing care in low-resource settings to break intergenerational cycles of disadvantage
Factors influencing asymmetries in Saudi Arabia's housing market
No full textThe article aims to examine the determinants of the asymmetries in the housing prices index (HPI) in 13 Saudi Arabian administrative regions. The authors employ a panel Vector Auto-regressions during the period 2014Q1-2023Q4 to measure the role of speculative and fundamental determinants in HPI growth across 13 regions. Furthermore, we use Least Square Dummy Variable method for the period 2015–2021 to analyze the asymmetry impact of region-specific determinants (economic, demographic, urbanization, geographic, and cultural variables) on HPI growth in 13 admirative regions. The results from the panel VAR model show that the reginal house prices growth is determined by the backward-speculative component (HPI's past values), the forward-looking speculation (Consumer Confidence Index), and the fundamentals variables (oil prices, employment, real estate loans, regional inflation, money supply, and building cost index). Furthermore, cross-section analysis using LSDV method reveals that the asymmetries in the HPI growth across 13 administrative regions is determined by the region-specific variables. These include backward-looking behavior, inflation, labor participation, population, health services quality, household size, inverse land supply, seaside density, temperature, and culture density. This study offers three key contributions to the literature. First, to the best of the author's knowledge, this is the first study that analyzes the asymmetries across Saudi Arabian regional housing markets. Second, while most studies focus on backward-looking speculation, overlooking forward-looking speculative factor, this analysis includes both. Third, the climate, geographical, and cultural determinants are largely ignored by the literature but this study incorporates these variables in the cross-section analysis
Diagnostic Precision: Validating the Oral Disease Recognition Scale
No full textObjective: To develop and validate a scale to assess the ability of dentists to recognize the clinical presentation of oral disease encompassing a range of benign, potentially malignant, and malignant conditions. Methods: The research employed a cross-sectional online survey methodology. Following ethical approval, a scale was developed by the research team and consisted of seven patient cases encompassing a range of benign, premalignant, and malignant oral conditions. The scale was pretested, and the external validity of the scale was evaluated. Results: Responses were received from a total of 254 participant. Interrogation of case performance data overall and by respondent subgroups, as well as consideration of response profiles, item analysis characteristics, reliability, and exploratory factor analysis support the existence of a single underlying construct that is measured consistently by the seven cases across respondent groups. Conclusions: This study developed and validated a scale to evaluate the abilities of dentists and oral health care professionals to recognize oral benign, premalignant, and malignant disorders. The scale may serve as a useful tool to assess clinicians' ability to recognize oral cancer and identify potential cognitive biases in their diagnostic approach
A Dynamic Ensemble Deep Randomized Neural Network Using Deep Autoregressive Features for Wave Height Forecasting with Missing Values
No full textWave energy is an essential part of sustainable energy. Precise forecasts of wave height assist in the reliable control of wave energy converters and the intelligent operation of electricity generation. However, the severe and extreme environment poses a significant challenge for accurate sensor recording, resulting in a huge number of missing values at random. The missing values exist in multiple explanatory variables, significantly deteriorating the performance of the classical machine learning models. This article aims to enhance the accuracy of significant wave height forecasting with data imperfections by proposing a flexible dynamic ensemble framework and an ensemble deep randomized neural network. First, the proposed dynamic ensemble framework disaggregates the whole forecasting task into multiple subtasks based on the number of missing values. For each subtask, any missing values imputation method can be employed due to the strong flexibility of the dynamic ensemble framework. This article proposes a novel ensemble deep random vector functional network with deep autoregressive features (DARedRVFL) as a base learner under the dynamic ensemble framework. The deep autoregressive features assist in extracting temporal features. Finally, combining the proposed dynamic ensemble and DARedRVFL achieves the minimum average rankings
APPROACHES OF MEANINGFUL ENGAGEMENT OF PEOPLE LIVING WITH CANCER WITHIN THE COUNTRIES OF THE WORLD HEALTH ORGANIZATION REGIONAL OFFICE FOR THE EASTERN MEDITERRANEAN: SCOPING REVIEW
No full textBackground: Cancer remains a major global public health challenge, contributing significantly to morbidity and mortality. People living with cancer (PLWC) meaningful engagement has emerged as an important tool for high-quality, patient-centred care, as well as for enhancing decision-making, treatment adherence, and overall quality of life. Aim: To understand the extent of evidence in relation to PLWC meaningful engagement strategies within the countries of the world health organization regional office for the eastern mediterranean (WHO EMR). Methods: This review was conducted in accordance with the JBI methodology for scoping reviews. The inclusion criteria used the PCC framework (Participants: PLWC, Concept: meaningful engagement approaches as defined by the WHO framework for meaningful engagement of people living with noncommunicable diseases and mental health and neurological conditions, and Context: countries of WHO EMR. A comprehensive search was conducted in February 2025 across key databases, including PubMed, Scopus, Embase, CINAHL, Cochrane, Index Medicus for the Eastern Mediterranean Region (IMEMR-WHO), and grey literature [ProQuest Central (Conference Proceeding/Dissertation/Thesis) and Google Scholar]. Studies were selected through a two-step process, initially screening titles and abstracts, followed by full-text reviews. Data was extracted using a standardized form. Evidence was synthesized narratively, with key findings presented thematically and in tables. The studies were arranged under the six enablers of the framework: Integrated Approaches, Redistributing Power, Building Capacity, Eliminating Stigma, Institutionalizing Engagement, and Sustainable Financing Results: A total of 614 citations were screened, resulting in 51 studies meeting the inclusion criteria. The studies spanned ten WHO EMR countries. Breast cancer was the most studied condition. The findings highlight variability in strategies including the 6 enablers of the WHO framework for meaningful engagement of people with lived experience. Integrated approaches being the most examined, while sustainable financing mechanisms were absent from the literature. Studies also highlighted the involvement of caregivers and healthcare professionals. Despite barriers, such as ethical concerns and lack of structured frameworks, patient involvement in research and treatment planning was emphasized. Conclusions: We need to mainstream the issue of meaningful engagement in the region through collaboration platforms, feature best practices and leaders from PLWC community, and improve commitment within the health system and governmental bodies to involve PLWC across the continuum of cancer care
Intranasal and Pulmonary Lipid Nanoparticles for Gene Delivery: Turning Challenges into Opportunities
No full textDelivery of nano-therapeutics through the nasal route offers a promising approach for several applications, including intranasal conditions, pulmonary delivery, brain targeting, and vaccination. Despite its potential, this method faces significant challenges, including overcoming the mucosal barrier, ensuring consistent absorption, controlling the deposition area, and managing immunogenic responses. This review provides a comprehensive overview of the current state of nasally delivered lipid nanoparticles (LNPs) for gene medicine, focusing on the specific barriers encountered in this delivery route and strategies to overcome them. We examine how formulation composition affects stability during aerosolization, analyze the impact of particle characteristics on mucociliary clearance, and evaluate interactions with the lung surfactant layer. The review also compares delivery devices including metered-dose inhalers, dry powder inhalers, and nebulizers, highlighting how device selection influences LNP integrity and deposition patterns. Furthermore, we explore potential safety considerations with intranasal LNPs and propose approaches to mitigate adverse effects. By addressing these challenges with evidence-based strategies, this review aims to advance the development and clinical application of intranasal and pulmonary LNP delivery systems for gene-based therapeutics and vaccines.This research was funded by the Qatar Research Development and Innovation Council (QRDI) under the Academic Research Grant (ARG) program, grant number ARG01-0522-230279.Scopu
Artificial intelligence in airway management: A systematic review and meta-analysis
No full textBackgroundAirway management is the cornerstone of anesthesia care. Complications of difficult airways are usually fatal to patients. Artificial intelligence (AI) has shown promising results in enhancing clinicians' performance in various settings. We therefore aimed to summarize the current evidence on the use of AI models in the prediction of a difficult airway. MethodsWe searched two databases, PubMed and Science Direct, for all relevant articles published until March 2025. Statistical software R version 4.4.2 was then utilized to meta-analyze the area under receiver operating curves (AUROC) to identify the best-performing models. ResultsAfter the eligibility assessment, 13 studies met the inclusion criteria and were thus included in the review. Only two studies developed models for patients in the ED, and the remaining 11 studies developed models for patients undergoing different surgeries under general anesthesia. The deep learning model with the best discriminative ability for difficult airways was VGG (AUC 0.84; 95% CI [0.83, 0.84] I2 = 0%). For the traditional machine learning models, those with good discriminative ability for difficult airways included SVM (AUC 0.80; 95% CI [0.65, 0.96] I2 = 99.7%) and NB (AUC 0.81; 95% CI [0.51, 1.10] I2 = 99.3%). ConclusionsOur study found that while some AI models have good discriminative ability (AUC ≥ 0.80) for difficult airways, most of them have just average discriminative ability AUC < 0.80. This, therefore, indicates a need to develop models with better discriminative ability and to validate the developed models
2025-12- En Version
No full textCampus Life is a biannual magazine issued by the QU Communications and Public Affairs Directorate
FIFA World Cup Qatar 2022: Exploring Volunteer Recruitment in Mega Events
No full textThe case study examines Nabeel Yahia's role in revolutionizing volunteer recruitment and management for the FIFA World Cup Qatar 2022. As a renowned academic transitioning to event management, Nabeel spearheaded the development of research-based strategies and training programs to enhance the volunteer experience. The case highlights implementing the e-portal recruitment system and e-learning programs and integrating local and international volunteers. It showcases the planning and execution of recruiting and training 20,000 volunteers from diverse backgrounds. The approach utilized proved effective in volunteer selection, role-specific training, and integrating various aspects of the World Cup. The case also underscores the impact of volunteer recruitment strategies, paving the way for future advancements in event management and community engagement beyond the FIFA World Cup Qatar 2022.Scopu
EXPLORING THE DIFFERENT WAYS ARTIFICIAL INTELLIGENCE IS USED IN LEARNING: A STUDY ON MASTER STUDENTS AT QATAR UNIVERSITY
No full textThis following MA study explores the different ways Artificial Intelligence (AI) is used by students who are enrolled in the College of Arts and Sciences master's degree programs at Qatar University. To collect data on the students' experiences, perceptions, and challenges associated with AI usage. The study used both qualitative and quantitative for the methodology, a survey and interviews. The study interviewed 15 students, and these interviews were one-on-one. For the survey, there were 96 replies received. It draws on two theoretical frameworks: Constructivist Learning Theory and Self-Directed Learning Theory. The thesis explores how MA students at Qatar University are integrating AI technologies into their academic learning, revealing that AI tools are widely used to support various tasks. The findings align with both Constructivist Learning Theory and Self-Directed Learning Theory, showing that students actively engage with AI to build knowledge, set learning goals, and reflect on their progress. Nonetheless, the study also identifies some limitations within these theoretical frameworks. Moreover, some students expressed concerns about ethical issues, including data privacy, algorithmic bias, and the risk of overreliance on technology. To address these challenges, the thesis provides recommendations for raising awareness about AI biases, creating institutional ethics guidelines, and offering ongoing education on AI use and digital literacy