Introduction: World Health Organization defines adverse drug reactions (ADRs) as any noxious, unintended, and undesired effect of a drug, which occurs at doses used in humans for prophylaxis, diagnosis, or therapy. For evaluating the incidence and outcome of adverse drug reactions (ADRs) to assist in minimizing the deleterious effects, the present study was planned to find the incidence of ADR, its severity, and outcomes among patients. Material and Methods: The spontaneous ADR reporting technique and the Suspected Adverse Drug Reaction Reporting Form were used for the data collection and reporting. All patients who developed an ADR during the study period has included. By using Expanded Rawlins and Thompson’s classification, all patients were categorized into types A to F and classified according to the severity levels (mild, moderate, severe) by applying the Modified Hartwig severity scale. The classification of outcomes of the ADRs was done as per WHO criteria as fatal, continuing, recovering, recovered, unknown, or any other. Results: Type B (Bizarre) ADRs were found to be the most frequently occurring ADRs (51%) followed by type A (Augmented) 29%. Maximum ADR cases were found in the 12-45 years age group (58%).

Background: The coronavirus disease 2019 has been spread almost all over the world in the last two years, including in India. Vaccines are a critical tool in the battle against COVID-19, and India has flagged the largest vaccination drive on 16 January 2021. Although public acceptance was varying, which can lead to non-acceptance. Aim & Objective: To estimate an acceptance of the COVID-19 vaccine and its associated factors. Settings and Design: An analytical cross-sectional study among health care workers in India   Methods & Material: It was conducted using a validated, self-administrated online survey questionnaire, and data were analyzed using SPSS 23 version. The outcome variable was healthcare workers’ acceptance of a COVID-19 vaccine. Results: A total of 450 HCWs participated, including 205(45.6%) women and 245(54.4%) men. A total of 270 (60%) subjects will accept vaccines, while 33.3% were unwilling to accept and wait for vaccines. Male gender (OR=3.14), being married and experienced (OR=11.49), vaccine effectiveness (OR=6.4), vaccine safety (OR=3.4), and past history (OR=2.28) were significantly associated. On applying logistic regression for associated factors, gender (B= -1.145, S.E.= 0.200, Wald 32.748), being married (B= -1.482, S.E.= 0.216, Wald 46.937), for experienced (B= -0.865, S.E.= 0.200, effectiveness (B= -1.856, S.E.= 0.245, Wald 57.431), Safety (B= -1.224, S.E.= 0.202, Wald 36.633) and past history (B= -0.357, S.E.= 0.248, Wald 2.071) found significant. Recommendation: Proper information is crucial and healthcare workers’ attitudes about vaccines are an important factor for acceptance and recommendation of the vaccine to the public for population-wide coverage.

Machine learning methods have been explored for cheating detection in large-scale assessment programs. While some studies analyzed item response and response time (RT) data, a few studies experimented with other data and data augmentation in cheating detection. However, none study has explored data augmentation integrating results from both psychometric analysis and machine learning for cheating detection though Kim et al. (2016) compared the results from these two perspectives. This study explored data augmentation in the stacking learning for cheating detection adding both person-fit measures from psychometric analysis and outlier measures from anomaly detection methods. An empirical data set from a high-stake large-scale testing program is used to demonstrate the application of the proposed method. Class imbalance is addressed via resampling. The performance of the proposed method is compared with alternative approaches. It is found the proposed data augmentation approach effectively increases the cheating detection accuracy with the highest F1 score compared with the values reported in similar studies.

A common situation that occurs in everyday life is that of queuing or waiting in line. The problem of queuing in relation to the time spent by patients to access clinical services is increasingly becoming a major source of concern to most health –care providers. As the patients wait too long for service could result to cost to them which is called as waiting cost. Providing good service capacity to operate a system involves excessive cost. But not providing enough service capacity results in excessive waiting time and cost. In this study, the queuing characteristics at the tertiary care hospital of the Autonomous State medical College, Firozabad was analyzed using a multi-Server queuing Model and the Waiting and service Costs determined with a view to determining the optimal service level. Data for this study was collected at the tertiary care hospital for four weeks through observations, interviews and by administering questionnaire. The data was analyzed using TORA optimization Software as well as using descriptive analysis.