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Background The COVID-19 pandemic considers a threat to students’ well-being and mental health. The current descriptive cross-sectional study aims to identify psychological distress among school students during the lockdown period. Methods This study was carried out in a sample of 420 primary and secondary school students from June 10 to July 13, 2020, in the Gaza Strip in Palestine. Data was collected using an online questionnaire that included informed consent, socio-demographic questions, and a psychometric scale (DASS-21). Results The results revealed that most students experienced moderate to severe levels of anxiety (89.1%) and depression (72.1%), whereas less than half of them (35.7%) experienced moderate to severe stress. Stress, anxiety and depression scores were significantly different across gender, age groups, family size, and family's economic status. The results showed that gender (β = -0.174, p  < 0.001), age (β = −0.155, p  = 0.001) and economic level of family (β = −0.147, p  = 0.002) were negative predictors correlated with stress. Family size (β = 0.156, p  = 0.001) played a positive role in stress. It was found that gender (β = −0.105, p  = 0.031), age (β = −0.135, p  = 0.006) and economic level of family (β = −0.136, p  = 0.005) were negative predictors correlated with anxiety, whereas family size (β = 0.139, p  = 0.004) played a positive role in anxiety. For depression, gender (β = −0.162, p  = 0.001), age (β = −0.160, p  = 0.001) and economic level of family (β = −0.131, p  = 0.007) were negative predictors correlated with depression, whereas family size (β = −0.133, p  = 0.006) was found to be a positive predictor. Concerns about the influence of COVID-19 on economic, education, and daily life were positively correlated to the levels of depression, anxiety and stress, whereas the availability of social support was negatively correlated. Conclusion The development of a health protocol for influenced students is urgently needed to maintain them remain resilient during dangerous times.

Background The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students’ attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. Methods This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022–2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. Results From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) ( p  < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. Conclusion Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

During the outbreak of the COVID-19 pandemic, digital learning has reshaped mathematics education in different ways. In particular, different social media platforms have acquired an unforeseen prominence as a way to enhance mathematics learning and to model somehow the face-to-face classroom interactions abruptly interrupted. How primary and secondary students have reacted or responded to these changes in the initial learning conditions is the main aim of this study. With this purpose in mind data was collected from 3179 students from the Gaza Strip by means of a validated rating scale and then a cluster analysis approach was applied that revealed the existence of three clusters. K-means cluster analysis was applied to analyze data as an exploratory approach to identify structures within the data. Also, chi-square was applied to identify differences between the clusters with regard to demographic characteristics. Regarding the grouping of participants in clusters the analysis performed lead to the identification of three clusters: Cluster 1, 2 and 3 had 2001, 824 and 354 cases, respectively. These clusters were grouped depending on employ social media platforms used by the students to reinforcement their mathematics learning. Participants in Cluster 3 exhibited the highest proficiency in the usage of social media for mathematics learning as compared to those in Cluster 1 and Cluster 2. This means that students in cluster 1 are more likely to exhibit negative attitudes and low levels in the skills related to using digital technology and the employment of social media in mathematics learning. The results showed that there were no significant differences between cluster-groups with regard to gender, age, and type of school. In contrast, significant differences were found among the three clusters with regards to the educational level of parents and the economic status of the family. However, the overall results show that digital learning is considered a positive response to the school closure in the time of the COVID-19 outbreak.

In response to the coronavirus disease 2019 (COVID-19) pandemic, many countries had implemented school closures by March 6, 2020. This study aimed to evaluate the social and economic impact of school closure on the students’ families. Households were surveyed using an online questionnaire interview to obtain information on adherence to, socio-economic impact by and inconveniences of school closure. The current study showed that school closures have profound economic and social consequences in the Gaza Strip. Most of the interviewed households (88.1%) were supportive of the school closure, whereas only 11.9% did not support it. Despite the restriction on attending gatherings or visiting public places, 30.5% of the school student visited relatives, 8.5% went to public places, and 3.4% went to parents’ workplaces. Overall, 25.4% of the interviewed households reported workplace absenteeism, whereas the highest percentage (74.6%) were not absenteeism from their work. The economic harms of school closures are high, where 77.9% of households reported their wage loss during the closure. The daily wage lost per household ranged from 3 to 265 ILS.

In recent decades, a variety of organizational sectors have demanded and researched green structural materials. Concrete is the most extensively used manmade material. Given the adverse environmental effect of cement manufacturing, research has focused on minimizing environmental impact and cement-based product costs. Metakaolin (MK) as an additive or partial cement replacement is a key subject of concrete research. Developing predictive machine learning (ML) models is crucial as environmental challenges rise. Since cement-based materials have few ML approaches, it is important to develop strategies to enhance their mechanical properties. This article analyses ML techniques for forecasting MK concrete compressive strength (fc’). Three different individual and ensemble ML predictive models are presented in detail, namely decision tree (DT), multilayer perceptron neural network (MLPNN), and random forest (RF), along with the most effective factors, allowing for efficient investigation and prediction of the fc’ of MK concrete. The authors used a database of MK concrete mechanical features for model generalization, a key aspect of any prediction or simulation effort. The database includes 551 data points with relevant model parameters for computing MK concrete’s fc’. The database contains cement, metakaolin, coarse and fine aggregate, water, silica fume, superplasticizer, and age, which affect concrete’s fc’ but were seldom considered critical input characteristics in the past. Finally, the performance of the models is assessed to pick and deploy the best predicted model for MK concrete mechanical characteristics. K-fold cross validation was employed to avoid overfitting issues of the models. Additionally, ML approaches were utilized to combine SHapley Additive exPlanations (SHAP) data to better understand the MK mix design non-linear behaviour and how each input parameter’s weighting influences the total contribution. Results depict that DT AdaBoost and modified bagging are the best ML algorithms for predicting MK concrete fc’ with R2 = 0.92. Moreover, according to SHAP analysis, age impacts MK concrete fc’ the most, followed by coarse aggregate and superplasticizer. Silica fume affects MK concrete’s fc’ least. ML algorithms estimate MK concrete’s mechanical characteristics to promote sustainability.

Background Depression, the leading cause of disability worldwide, has worsened due to the COVID-19 pandemic, with key risk factors including age, gender, and socioeconomic status. Jordan is experiencing an increase in mental health issues, particularly among children, adolescents, and university students, yet negative attitudes and structural barriers limit access to mental healthcare This study provides a comprehensive analysis of depression, anxiety, and sleep quality among Jordanian undergraduate students. Methods This cross-sectional study was conducted between December 2023 and March 2024. The study used the Generalized Anxiety Disorder scale (GAD-7), the Patient Health Questionnaire (PHQ-9), and the Insomnia Severity Index (ISI) to evaluate these mental health factors. Data collection involved a self-administered survey, distributed online and in paper format. Multiple linear regression was utilized to pinpoint significant predictors. Results A total of 1181 participants completed the survey, with an average age of 20.43 years. Among the respondents, 74.2% were female, and 35.1% were medical students. Predictors of GAD-7 scores included year of study for medical students, with PHQ-9 scores and male gender serving as negative predictors. For non-medical students, both PHQ-9 and ISI scores positively predicted higher GAD-7 scores. ISI scores were positively influenced by age and PHQ-9 scores for medical students, whereas for non-medical students, age, PHQ-9, and GAD-7 scores were positive predictors. Conclusion The study demonstrates that non-medical students experience higher levels of depression, insomnia, and anxiety than their medical counterparts. These findings highlight the necessity for targeted mental health interventions and awareness programs for all undergraduate students.

Background Postpartum depression (PPD) is a form of depression that can occur after childbirth and is characterized by feelings of sadness. It is a common psychological problem that affects women and children. This study aimed to assess the association between PPD and risk factors, such as delivery mode, ABO blood group, and passive smoking in Saudi Arabia. Methods PPD was assessed in this cross-sectional using an Arabic version of the Edinburgh postnatal depression scale through an online questionnaire distributed to women in Saudi Arabia between January and March 2022. The data were analyzed using SPSS version 26 (IBM Corp., Armonk, NY). Results A total of 354 postpartum women completed the questionnaire within six weeks of giving birth. Their mean age and BMI were 30.1±6.78 years and 25.98±5.84 kg/m , respectively. PPD occurred in 56.2% of the participants. Elective cesarean section and operative vaginal delivery were associated with the presence of PPD symptoms in 17.6% and 7% of the women, respectively. The majority of those with third and fourth degrees and those who had instrumental assisted delivery had postpartum depression and this was statistically significant (p=0.017). About 26.6% of the participants were exposed to passive smoking, and 21.9% of them developed PPD. However, it was not statistically significant. Moreover, women with PPD were more likely to have blood type O+, followed by A+. Demographic factors did not show a significant correlation with developing PPD except for age (p=0.01), those who developed PPD were much younger on average than those who did not develop PPD (29.28±6.61 years vs. 31.15±6.86 years). Conclusion A significant association was found between PPD and the type of delivery. The association between PPD and passive smoking, ABO blood groups was insignificant. However, women who developed PPD were younger on average than those who did not develop PPD.

Background: The successful anesthesia is an essential factor for dental treatment. Fear of local anesthesia is a significant barrier to dental care as many patients delay or avoid treatment to prevent pain. It is important to evaluate the relationship between dental anxiety, state anxiety and procedure pain during maxillary local anesthesia.The study aimed: Toevaluate the correlation between dental anxiety, state anxiety and pain after dental injection. Methods: A cross sectional study was utilized. Before receiving treatment, every one of the 120 children who scored highly on the Frankle behavior measure had a maxillary injection. The Children Fear Scale was used to measure dental fear, while the Children fear Questionnaire was used to measure state anxiety. Pain following dental injection was measured using the FLACC Scale and the Wong Baker Scale. Results: No statistically significant differences were found in anxiety level scores between genders and age groups with p-value > 0.05. A statistically significant difference in the postoperative pulse rate between males and females, with females having a greater mean pulse rate (pvalue=0.024). A positive correlation was found between each dental anxiety and procedural pain and state anxiety and procedural pain.Conclusion: Reducing anxiety in young patients improves their quality of life and health care by lowering the expected and procedural pain that they endure.