Browsing by Author "Joshi, Deepak Raj"
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Publication Nutritional Status and Its Associated Factors among Under-Five Children in Bidur Municipality, Nuwakot District, Nepal: A Secondary Data Analysis(Central Department of Public Health, 2024) Pradhan, Pranil Man Singh; Pant, Smriti; Shrestha, Gambhir; Karki, Namrata; Mikrani, Tanweer Ahmad; Joshi, Deepak Raj; Shrestha, Sushan Man; Basel, Prem; Karki, Khem B.Abstract Background: Undernutrition among under-five children is a major public health problem in Nepal. The study's main objective was to assess the nutritional status and its associated factors among under-5 children of Bidur Municipality, Nuwakot district using secondary data analysis. Methods: We analyzed the data of 667 under-five children from 3183 households collected from the Community Health Diagnosis 2023 program database stored in the Kobo toolbox. Nutritional status indicators were measured by using the WHO classification. The chi-square test was applied to find out the association of nutritional status with independent variables such as maternal age at marriage, age at first child, antenatal, delivery, and post-natal care, exclusive breastfeeding, complementary feeding practices, and immunization status. Results: Nearly 32% (95%CI: 28.3-35.8) of the children were stunted, 12.1% (95%CI: 9.7-14.7) wasted, and 13.9% (95%CI: 11.4-16.6) were underweight. Early age at marriage, less than four ANC visits by the mother, history of substance use during pregnancy, and not exclusively breastfeeding were significantly associated with different forms of undernutrition. Conclusion: Promotion of exclusive breastfeeding and a higher number of ANC visits and discouraging early-age marriage and substance use during pregnancy are necessary to prevent undernutrition in under-five children. Keywords: Malnutrition, Nepal, under-five childrenPublication Perception and Willingness Regarding Organ Transplantation/donation among Medical Students of Maharajgunj Medical Campus: A Cross-sectional Study(Nepal Health Research Council, 2024) Bhattarai, Amit Sharma; Adhikari, Bidur; Joshi, Deepak Raj; Yadav, Alisha; Joshi, Pankaj; Parajuli, Bashu Dev; Koirala, Megha; Karki, Kushal Jung; Subedi, Krishna Prasad; Shrestha, Gentle Sunder; Shrestha, Anil; Singh, ShreejanaBackground: Organ transplant is the procedure of replacing a failing or damaged organ with a functioning one. Positive attitude and awareness about donation are a must for donor organs to be available. This study explored the level of knowledge, perception and willingness regarding organ donation among medical students in Nepal. Methods: A descriptive cross-sectional study was conducted with 180 medical students using a self-administered questionnaire. Descriptive statistics were used, and Pearson correlation was applied to examine the relationship between knowledge and perception of organ transplantation. Independent samples t-test and ANOVA was used to compare scores among year of study and gender. Results: Results indicated that 86.1% of participants were aware of the need for organ donation, and 83.3% knew that both living and deceased individuals could be donors. While 93.9% believed in the need for effective laws, 72.8% perceived risks for donors. However, only 74.4% were willing to donate their organs, though 91.7% expressed willingness to promote organ donation among friends and family. Participants showed a positive perception towards organ donation. Conclusions: Despite a high level of awareness and knowledge regarding organ donation, participants exhibited a lower level of willingness to donate organs. Perceived risks for donors and a lack of robust laws and regulations presented significant barriers. Nevertheless, an inclination to promote organ donation was observed. This underlines the need for enhanced education and policy reform to increase organ donation rates. Keywords: Awareness; knowledge; organ donation; organ transplantation; perception.Publication Why Traditional Statistical Methods Need to Evolve in the Age of Artificial Intelligence: A Biostatistical Perspective(Central Department of Public Health, 2025) Joshi, Deepak RajTraditional statistical methods, basically the frequentist approach, must evolve to remain relevant in the age of Artificial Intelligence (AI). While Conventional statistical methods work under theoretical assumptions, they struggle to handle the complexities of modern biomedical data, including high dimensionality, non-linearity, and violations of key assumptions However, this is not a problem for the newer machine learning models like support vector machines. There are new techniques like regularization (ridge, lasso) to handle many of the assumptions in traditional statistical methods, which can be implemented and automated using software like R and Python. Machine learning as a part of AI offers solutions by handling large-scale complex datasets, uncovering hidden patterns, and improving prediction power. They are based on the foundation models where statistics and mathematics meet. So, just talking about the limitations of the statistical methods is half true. The viewpoint tries to explain why to integrate AI with traditional biostatistics, creating hybrid models that combine statistical rigor with AI flexibility. Integration can enhance data analysis, causal inference, and decision-making, ultimately advancing personalized medicine and public health, ethically and transparently.