The rapid spread of the Covid-19 outbreak led many countries to enforce precautionary measures such as complete lockdowns. These lifestyle-altering measures caused a significant increase in anxiety levels globally. For that reason, decision-makers are in dire need of methods to prevent potential public mental crises. Machine learning has shown its effectiveness in the early prediction of several diseases. Therefore, this study aims to classify two-class and three-class anxiety problems early by utilizing a dataset collected during the Covid-19 pandemic in Saudi Arabia. The data was collected from 3017 participants from all regions of the Kingdom via an online survey containing questions to identify factors influencing anxiety levels, followed by questions from the GAD-7, a screening tool for Generalized Anxiety Disorders. The prediction models were built using the Support Vector Machine classifier for its robust outcomes in medical-related data and the J48 Decision Tree for its interpretability and comprehensibility. Experimental results demonstrated promising results for the early classification of two-class and three-class anxiety problems. As for comparing Support Vector Machine and J48, the Support Vector Machine classifier outperformed the J48 Decision Tree by attaining a classification accuracy of 100%, precision of 1.0, recall of 1.0, and f-measure of 1.0 using 10 features.

Objective: To assess the prevalence of anxiety and factors associated with it during the peak of the outbreak in Saudi Arabia. Materials and Methods: This cross-sectional research screened the general public using the Generalized Anxiety Disorder Scale-7 to detect anxiety levels. The questionnaire was distributed online during May 2020, while lockdowns were enforced. A total of 3017 respondents from all five main regions of Saudi Arabia completed the survey. The prevalence of anxiety was measured. Chi-square and logistic regression analyses were executed to determine associated factors with anxiety during peak lockdown. Results: About 19.6% of the respondents possessed a moderate to severe level of anxiety during the pandemic. Western, Northern, and Eastern regions of Saudi Arabia were found to be the most anxious. Female participants had 5.3% higher levels of anxiety compared to male counterparts. The youngest age group (18 to 19 years), most of them were students, reported the highest frequency of anxiety (28.7%). Divorced and single participants had a higher level of anxiety compared to married ones. After adjusted with other variables, living with a family member with risk of the COVID-19 was the best predictor assessing anxiety amid peak lockdown (OR: 1.8, 95% CI: 1.4– 2.2). Conclusion: Notable anxiety prevailed during the initial phase of the COVID-19 outbreak in Saudi Arabia. The presence of vulnerable subjects in the family augments this psychological disorder considerably. Our findings promulgate a need to inculcate nation-wide strategies to enforce public health emergency preparedness plans to mitigate the adverse psychological effects of outbreaks.

The Saudi population is at high risk of multimorbidity. The risk of these morbidities can be reduced by identifying common modifiable behavioural risk factors. This study uses statistical and machine learning methods to predict factors for multimorbidity in the Saudi population. Data from 23,098 Saudi residents were extracted from the “Sharik” Health Indicators Surveillance System 2021. Participants were asked about their demographics and health indicators. Binary logistic models were used to determine predictors of multimorbidity. A backpropagation neural network model was further run using the predictors from the logistic regression model. Accuracy measures were checked using training, validation, and testing data. Females and smokers had the highest likelihood of experiencing multimorbidity. Age and fruit consumption also played a significant role in predicting multimorbidity. Regarding model accuracy, both logistic regression and backpropagation algorithms yielded comparable outcomes. The backpropagation method (accuracy 80.7%) was more accurate than the logistic regression model (77%). Machine learning algorithms can be used to predict multimorbidity among adults, particularly in the Middle East region. Different testing methods later validated the common predicting factors identified in this study. These factors are helpful and can be translated by policymakers to consider improvements in the public health domain.

Although higher education typically strengthens people with disabilities’ chances to be in a competitive job market as a leverage to break away from poverty, the contemporary marketization of higher education within the era of neoliberalism has made degrees and diplomas increasingly unaffordable. The federal government responded to this predicament by increasing the Canada Social Transfer to offer up-front targeted funding to students with disabilities in 2008. However, virtually no literature has acknowledged whether financial grants meaningfully provide students with disabilities with an equitable opportunity to engage in post-secondary education. Using critical discourse analysis on government online materials that address federal and provincial disability grants for post-secondary students with disabilities in Alberta and Ontario, this study reveals how the neoliberal rhetoric of personal responsibility shapes the practice of government disability grants. This practice can leave students with ‘more responsibility’ while rendering ‘less control’ over their finances in an already disabling world.

Objectives: Value-based healthcare (VBHC) represents a paradigm shift in healthcare delivery through optimizing patient outcomes relative to the costs of achieving those outcomes. This scoping review is aimed at revealing critical insights into the conceptualization and establishment of VBHC in the context of Saudi Arabia, a nation in a critical stage of healthcare transformation. Methods: A scoping review was conducted by using online databases and official websites with a timeframe of 2017-2023. This review included 14 pieces of literature, comprising six research articles, six government documents, and two reports. Results: The findings highlight increasing alignment with the definition of global VBHC principles, notably the emphasis on patient outcomes as a primary metric of healthcare value. Furthermore, financial reform has signaled a real move toward VBHC in the Kingdom, through a gradual shift from volume-based payments to value-based payments. However, the diverse interpretations and applications of VBHC across the examined literature indicate a promising stage of implementation characterized by evolving definitions and practices tailored to local needs and constraints. Conclusion: This scoping review describes the current landscape of VBHC conceptualization and establishment, highlighting the substantial progress achieved and the future challenges.