Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
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Innovative development is a program that is given at The Hague University of Applied Sciences. This program teaches students to become more innovative. This article will look into the current approach and measure the growth in innovativeness of the students over the years. This was measured with a survey, based on the Berkeley innovation index. The results from the survey were calculated and scored based on eight factors. The innovative development program was compared with another program called information security management. These programs are from the same faculty. The information security management program did not show significant growth over the years in innovation. The innovative development program had resulted in a significant growth in innovativeness over the years. Some of the factors could be improved to increase the effectiveness of the innovative development program. https://nl.linkedin.com/in/haniers
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This research evaluates how Project-Based Learning (PBL) is implemented in the Innovative Design program that is taught at The Hague University of Applied Sciences. This paper offers insights about the way students and teachers experience PBL within this program, and how the implementation can be improved according to previous research in this field. By studying relevant literature, a list of important (organizational and didactical) factors regarding the implementation of PBL is created. Questionnaires investigating these factors are then circulated among the teachers and students of the program. The results of the questionnaires are analyzed against guidelines provided in the literature. Based on this comparison, recommendations for the improvement of the PBL approach within the program are provided. The analysis shows that the program offers meaningful projects, and the students are properly prepared to collaborate. Nevertheless, the analysis also shows that the program still has room for improvement. The assessment methods are still unrefined, the students experience time-pressure while working on their projects, and the teachers can benefit from additional training to be better prepared for teaching in a PBL environment. Fortunately, the teachers indicate willingness to learn new PBL specific teaching skills. https://nl.linkedin.com/in/haniers
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