risk factors

Introduction: Wrestling is one of the most popular Olympic sports in Iran. Therefore, preseason screening and the prevention of sports injuries are very important. This study aimed to investigate the relationship between the history of injury and Functional Movement Screen (FMS) scores of the national team wrestlers and determine the cut-off point. Methods: The statistical sample included 136 national team wrestlers. The obtained data were analyzed using the Pearson correlation coefficient, t-test, ROC curve, and contingency table. Results: The results showed that FMS scores were higher in the wrestlers without previous injury compared to the injured ones. The t-test results demonstrated no significant difference between deep squat, straight and active leg raise, trunk stability push-up, and rotatory stability. According to the results, there is a poor negative, but statistically significant, the relationship between the number of previous injuries and FMS scores. Based on the ROC curve for FMS, the cut-off point of 16.5 was reported with the sensitivity and specificity values of 0.587 and 0.658, respectively. Conclusion: The results indicated that FMS can be used for fast and accurate control of injury probability in wrestling athletes. Therefore, besides the medical tests, FMS tests should be employed by wrestling coaches as a valid tool for injury prevention and the identification of athletes prone to injury.

This study represents an investigation into the interplay of the English as a Foreign Language (EFL) teacher burnout and such teacher-related variables as emotional intelligence, personality traits, teaching experience, self-efficacy, school type, gender, academic degree and age. This is a research report of a study on 124 secondary school EFL teachers with BA, MA, and PHD academic degrees. Findings of correlation and sequential multiple regression analyses suggest that EFL teacher burnout is likely to result from several factors with emotional intelligence being by far the strongest predictor of them. It was also found that emotional intelligence and self-efficacy have a negative moderate correlation with teacher burnout. Strong correlations were also found between emotional intelligence and self-efficacy, age and self-efficacy, and age and experience. No significant relationship was found between burnout and age, experience or personality. Some critical points are raised for the education system in Iran and practical implications are suggested.

Diseases like diabetes are chronic and require long-term management. Inadequate production of insulin results in high blood sugar levels. Such diseases lead to serious health issues such as heart ailments, blood vessel complaints, eye ailments, kidney function disorders, and nerve ailments. Hence, accurate assessment and management of risk factors are crucial for the onset of diabetes. Our proposed approach combines fuzzy logic & machine learning algorithms for diabetes risk prediction. Three machine learning models were trained to classify patients into two categories of diabetes (Type-I and Type-II) based on their clinical dataset collected from Katihar Medical College & Hospital and Suvadhan Lab. The polynomial regression algorithm achieved a score of 0.947, while the support vector regression algorithm with the rbf kernel achieved a score of 0.954, with a linear kernel achieved a score of 0.73. Our proposed approach performed well with respect to the conventional approaches with improved accuracy by identifying the patients at diabetes risk. In future work, we further analyze the relationship between other ignored factors which contribute to diabetes risk.