An application of ordinal regression to extract social dysfunction levels through behavioral problems.

Psychological problems are complex in nature and accurate identification of these problems is important. For the identification of psychological problems, one of the preliminary tools is the use of interviews/questionnaires. Questionnaires are preferred over interviews if the group under study is large. A strengths and difficulties questionnaire (SDQ) is one of the most widely used and powerful questionnaires to identify behavioral problems and distresses being faced by the respondents, affecting their day-to-day lives (responsible for social dysfunction). This study was held on college/university students in India, with the objective of examining if the extent of social dysfunction as measured by an impact score can be extracted from behavioral problems which are the components of the difficulty score of SDQ. Two surveys were conducted during the COVID-19 pandemic period, between the months of May-June 2020 and October 2020-February 2021 for the study. Only those responses were considered who felt distressed ("yes" to item 26 of SDQ). The numbers of such responses were 772/1020 and 584/743, respectively, in the two surveys. Distress levels were treated as ordered variables and three categories of distress level, viz., "Normal", "Borderline", and "Abnormal" were estimated through behavioral problems using ordinal regression (OR) methods with a negative log-log link function. The fitting of OR models was tested and accepted using Cox and Snell, Nagelkerke, and McFadden test. Hyperactivity-inattention and emotional symptoms were significant contributors to estimating levels of distress among respondents in survey 1 (p < 0.05). In addition to these components, in survey 2, peer problems were also significant. OR models were good at estimating the extreme categories; however, the "Borderline" category was not estimated well. One of the reasons was the use of qualitative and complex data with the least wide "Borderline" category, both for the "Difficulty" and the "Impact" scores.

Personality and achievement: A follow-up study.

Background: The health and well-being of medical students along with their successful progression is an important concern for society. Studies across the world demonstrated a high prevalence of psychiatric morbidity and low self-esteem among medical students requires immediate attention and effective management. Aim: To compare the symptoms of depression, anxiety, and stress between the groups who have cleared all papers in the first attempt with those who have got supplementary at least in one paper during their MBBS course. Materials and Methods: Raven's Progressive Matrices Scale, Eysenck personality Questionnaire, Sinha's Anxiety Scale, Stress Reaction Check (SRC) List, and 16-Personality Factors questionnaires were used along with performance score was recorded for the objective of the study. Results: Nonparametric tests, Mann-Whitney and Wilcoxon are used and found that RSPM scores and EPQ Lie-scale scores are statistically different as the P < 0.05. Overall performance and psychological problems association are found out using Spearman's correlation test and it is found that Factor C and Tough poise under 16 PF are significantly correlated. Conclusions: The study concluded that there is a significant difference between the abstract reasoning between the two groups of students measured by RSPM. When the students are compared by taking gender as grouping variable the study concluded psychosis, lie scale value is significantly different in the two groups and the mean value is higher in the female group. It is also concluded that the stress level measured by SRC is significantly different and the mean value is higher in the female group. Furthermore, the global factors Tough poise and independence are found to be significantly different in two groups with 16 PF questionnaire.

Modelling and forecasting Positive and Negative Syndrome Scale scores to achieve remission using time series analysis.

OBJECTIVES: Schizophrenia is a chronic mental condition. The objective of this study is to apply time series modelling to Positive and Negative Syndrome Scale scores of outpatients with schizophrenia, observed at regular intervals of time, and hence forecast the number of visits required to reach remission. METHODS: A retrospective data of outpatients diagnosed with chronic paranoid-type schizophrenia were extracted from the records of outpatient department of a tertiary hospital in New Delhi, India. Autoregressive integrated moving average (ARIMA) and ARIMAX models (ARIMA with explanatory variable as Clinical Global Impression Severity scale) are fitted to the data. The best fit models are employed to forecast the number of visits required to reach remission for the outpatients who did not achieve remission by the end of study. Prediction accuracy of the two models is compared using mean absolute percentage error and mean absolute deviation. RESULTS: The ARIMA (1, 2, 1) and ARIMAX (1, 2, 1) models are identified to be suitable models after a series of statistical tests. CONCLUSIONS: ARIMA and ARIMAX models are suitable to predict number of visits required to reach remission. Further, ARIMAX model performed better than ARIMA model.