In improving the quality of education, parents’ involvement is very significant. Parents’ involvement is one of the three responsibilities for realizing the best quality. This study attempts to explore parents’ involvement in education activities for improving their children’s academic achievement. A sample of 292 households was selected from whose students’ grade five to grade eight of four schools. Descriptive analysis showed that 19% of the respondents do not actively involve at all in giving advice, guiding, and counseling their children at home. 67% of the respondents showed medium parental involvement in helping their children, while 14% of respondents showed a good status of parental involvement in education at home. Similarly, the study revealed that 53% of the respondents were not participating in school activities at all and 35% of them were medium participation and 13% of them were well involved in school activities. The multiple linear regression revealed that the gender of the household head, educational level of the father, and sources of income of parents were found to be the major predictors of parents’ involvement in education for improving academic achievements while the aging of the head of the household, number of children and gender of students were not showing significance. Government, school leaders, and policymakers should explore mechanisms to increase parents’ involvement in students’ learning and make it obvious to parents their attitude toward their child’s academic performance.

Background/Objectives: Artificial intelligence (AI) is increasingly integrated into healthcare decision-making. Public trust in AI remains a critical determinant of its acceptance and effective use. Evidence on the factors shaping trust in AI within Middle Eastern contexts, particularly Saudi Arabia, remains limited. Therefore, we aimed to identify the determinants of trust in AI for health-related decision-making and to examine a theory-informed mediation pathway in which patient satisfaction mediates the association between patient–doctor relationships and trust in AI. Methods: We conducted a cross-sectional, facility-based survey of adults in Saudi Arabia, using an electronic questionnaire distributed in four primary healthcare centers. We performed multiple linear regression to assess the association of trust in AI for health-related decision-making with patient satisfaction, patient–doctor relationships, sociodemographic characteristics, and healthcare-related factors. A mediation analysis was also employed to evaluate the indirect and direct association linking patient–doctor relationships, patient satisfaction, and trust in AI. Results: Our findings showed that patient satisfaction was positively associated with trust in AI (β = 0.54, 95% CI: 0.18–0.90), while patient–doctor relationships showed an inverse association (β = −0.34, 95% CI: −0.48 to −0.20), possibly reflecting a greater reliance on physicians’ clinical judgment and a reduced perceived need for AI-supported decision-making. Trust in AI varied across age groups, with a lower trust observed in older age categories compared with younger adults. No strong associations were observed for sex, education, body mass index, or healthcare-related factors. Patient–doctor relationship quality was indirectly associated with trust in AI via patient satisfaction (ACME = 0.138, 95% CI: 0.043–0.246), alongside a direct association with trust in AI (ADE = −0.313, 95% CI: −0.456 to −0.160). This means that patient–doctor relationships influenced trust in AI both directly and indirectly through patient satisfaction, suggesting that, while interpersonal care may reduce the reliance on AI (direct effect), enhancing patient satisfaction can partially offset this effect and promote trust in AI (indirect effect). Conclusions: These findings highlight that fostering patient-centered care and satisfaction may be crucial for promoting public trust in AI, which has important implications for AI governance, ethical deployment, and the design of AI-supported healthcare systems.

Introduction: The under-five mortality rate, often known by its acronym U5MR or simply as the child mortality rate, indicates the probability of dying between births exactly five years of age, expressed per 1,000 live births. In comparison, the probability of dying after the first month and before reaching age 1 was 12 per 1,000, the probability of dying after age 1 and before age 5 was 10 per 1,000, and the probability of dying after age 5 and before age 15 was 7 per 1,000. Objectives: The study was aimed to determine the major factors of child mortality in Ethiopia using different counting models. In detail the study has the objective of identifying the risk factors of child mortality in Ethiopia and also to prioritize the best counting models that fit the data well. Methods: The Ethiopian demographic and health survey of 2016 was used for this study. About 10641 women aged between 15-49 were included in the survey. To analyze the data, counting models like the Poisson regression model, negative binomial model, zero-inflated regression models, and zero-inflated negative binomial regression model were applicable. Results: The results of the study indicated that of the total 10641 women respondents, 7576 (71.2%) have not faced the problem of child mortality. Thus, this result has the clue that the count models, especially the models that can handle the dispersion may be applicable. The average rate of child mortality is less than the variance of child mortality and this indicated that there is an over-dispersion of the data. Of all the candidate models, a zero-inflated negative binomial regression model was found to be the best model since it has a minimum AIC(15517). The coefficient table of the best model indicated that of child mortality for the women from rural residence is 1.2532 greater than those from urban with a 95% confidence interval (0.0905, 0.3610). Conclusion: The model comparison technique is indicated that the zero-inflated negative binomial regression models were the best mode that fit the data well. Under this model, the residency of women, birth order, Preceding Birth Interval, Size of a child at birth (smaller than average), and number of household members are significant variables in determining the status of child mortality in Ethiopia

Climate changes are increasing the frequency of concurrent extremes in temperature, air pollution, and aeroallergens, yet evidence on their joint and synergistic health impacts remains limited. We aimed to quantify the independent, joint, and interactive short-term effects of temperature, air pollutants, and airborne pollen on allergic rhinitis and asthma using long-term general practitioner (GP) data. We conducted a population-based time-series study using 20 years of GP data. Daily maximum temperature, PM₂.₅, ozone, and pollen concentrations were linked to allergic rhinitis and asthma outcomes. We estimated cumulative relative risks (RR) over lag 0-14 days using distributed lag non-linear models, comparing high (95th percentile) versus median exposure levels. We evaluated effect modification through stratified analyses and quantified additive interaction for joint exposures at extreme levels (90th and 95th percentile) using relative excess risk due to interaction (RERI) and attributable proportion (AP). Pollen exposure was strongly associated with allergic rhinitis (RR=2.54, 95% CI: 2.40-2.69) and with asthma (RR=1.49, 95% CI: 1.38-1.61). In joint-effects analyses, co-exposure to extreme heat and high pollen concentrations was associated with an increased risk of allergic rhinitis (RR= 2.07, 95% CI: 1.77-2.41), with clear evidence of synergistic interaction on the additive scale (RERI=0.48, 95% CI: 0.32-0.64, AP=0.23, 95% CI: 0.17-0.30). Similarly, co-exposure to high pollen and ozone was associated with elevated allergic rhinitis risk (RR = 2.03, 95% CI: 1.76-2.34), with positive additive interaction (RERI = 0.40, 95% CI: 0.25-0.54; AP = 0.20, 95% CI: 0.13-0.26). The same exposure combinations, heat-pollen and pollen-ozone, also exhibited positive synergistic interactions for asthma. Our findings identify pollen as a central driver of climate-sensitive allergic morbidity, with heat and ozone acting as key amplifiers through synergistic interactions. Our findings highlight the need for integrated early-warning systems, and risk assessments that account for joint environmental exposures.

Background: Psychological stress is a common concern among university students, which is also pronounced among nursing students due to the academic and clinical demands of their training. Persistent stress can negatively affect students’ mental well-being, academic performance, and professional development. Social support has been identified as an important protective factor. However, evidence on the relationship between perceived social support and stress among nursing students in Middle Eastern educational contexts remains limited. Methods: A cross-sectional survey was conducted among nursing students. The survey included the Multidimensional Scale of Perceived Social Support (MSPSS) and the 10-item Perceived Stress Scale (PSS-10), along with sociodemographic and academic characteristics. Multivariable linear regression was used to examine the association between perceived social support and perceived stress after adjusting for age group, sex, program type, living arrangement, and employment status. Differences in stress across levels of social support were also examined using analysis of variance (ANOVA). Results: A total of 182 nursing students participated in the study. The mean perceived social support score was 4.95 (SD = 1.42), while the mean perceived stress score was 15.49 (SD = 2.82). We found that higher perceived social support was significantly associated with lower perceived stress (β = −1.9, 95% CI: −3.4 to −0.44), indicating that a one-point increase in the MSPSS score was associated with a 1.9-point decrease in perceived stress. Other sociodemographic factors were not significantly associated with stress. ANOVA indicated significant differences in stress across social support levels (F(2,179) = 6.91, p = 0.001). Conclusions: Perceived social support was significantly associated with lower levels of perceived stress among nursing students. These findings highlight the potential importance of strengthening supportive social environments to promote psychological well-being in nursing education.

Background: Anemia in children is a serious public health issue in low‐income settings, particularly sub‐Saharan Africa. In the Democratic Republic of Congo (DRC), evidence on the geographic distribution and multilevel determinants of childhood anemia remains limited. This study aimed to examine the spatial pattern and identify individual, household, and community‐level determinants of anemia among children aged 6–59 months in the DRC. Methods: A secondary analysis was conducted using data from the 2023/2024 DRC‐DHS. A weighted sample of 9449 children aged 6–59 months was included. Spatial analyses were performed using Global Moran’s I, Getis‐Ord Gi∗, and Kriging interpolation techniques to assess geographic clustering and spatial patterns of childhood anemia. Multilevel binary logistic regression models were fitted to identify factors associated with childhood anemia, and adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. Results: Of the 9449 children, 6092 (64.5%) were anemic. Spatial analysis showed significant geographic clustering of anemia in the DRC (Moran’s I = 0.516, p = 0.0078), with hotspot regions in western‐central and cold spots in eastern provinces. Our findings showed that several factors were associated with lower odds of childhood anemia. Specifically, we observed reduced odds among children aged 24–35 months (AOR = 0.48; 95% CI: 0.34–0.68), females (AOR = 0.89; 95% CI: 0.81–0.98), and those from households in the middle wealth index (AOR = 0.86; 95% CI: 0.75–0.98). In contrast, higher odds of childhood anemia were observed among children with severe stunting (AOR = 1.19; 95% CI: 1.04–1.37), severe underweight (AOR = 1.58; 95% CI: 1.31–1.92), fever (AOR = 1.24; 95% CI: 1.10–1.40), those born to anemic mothers (AOR = 1.39; 95% CI: 1.26–1.53), children from households with female household head (AOR = 1.21; 95% CI: 1.08–1.35), and those residing in communities with higher levels of poverty (AOR = 1.24; 95% CI: 1.05–1.47). Conclusion: Childhood anemia in the DRC remains highly prevalent and spatially clustered, with western and northern provinces particularly affected. Individual, household, and community factors were identified as significant determinants of anemia risk. Targeted interventions focusing on high‐risk regions, nutritional support, maternal health, and socioeconomic improvements are critical to reducing childhood anemia in the DRC.