Introduction: Emotional mental disorder (EMD) is a state of psychological distress that, if not handled properly, can lead to severe mental disorders. One potential psychosocial hazard that is ...thought to be related to mental health disorders is work-family conflict (WFC). The issue of family-work balance has become an important issue among Indonesian female workers, especially with the rapidly growing female worker society and the strong family culture. This study aimed to identify the association between work-family conflict and emotional mental disorder in female nurses. Methods: This cross-sectional study included 264 female nurses who work at national referral general hospital in Jakarta. Their emotional mental disorder was measured using Self-Reported Questionnaire 20 (SRQ-20) and the work-family conflict was measured using the Work-Family Conflict Scale (WCFS). Results: The prevalence of emotional mental disorder in female nurses was 23.5%. The most dominant factor associated with emotional mental disorder is work-family conflict (OR 2,40, CI 95% 1,32-4,35, p=0,004). Conclusion: There is a significant association between work-family conflicts and emotional mental disorders in female nurses in Indonesia. Nurses with work-family conflicts are more likely to have emotional mental disorders. Regular mental health checks and counseling should be performed along with periodic health checks.
Background: The study aims to determine the difference in the proportion of nicotine dependence among high school students using Fagerstrom Test for Nicotine Dependece set in urban and rural ...environment.
Method: Sample size is 757 high school students from six different high school consists of class 1, 2 and 3 were selected based on stratified cluster random sampling was asked to fill out the question of smoking status and filling fagerstorm test for nicotine dependence if the respondent is smokers.
Result: Amount of 167 students with smoking status and nicotine dependence measured results obtained by 28 (16.8%) persons with nicotine dependence with 8 (11.1%) people in urban areas and 20 (21.1%) people in the rural area. Factors were statistically significant to nicotine dependence is gender, the originator, type of inhale, age first smoked and number of cigarettes smoked per day. CO levels of relationship with the level of nicotine dependence shows a strong and positive patterned.
Conclusion: There is a significant relationship between gender, the originator, type of inhale, age first smoked, number of cigarettes smoked per day to nicotine dependence and and level of CO exhalation to nicotine dependence. (J Respir Indo. 2017; 37(4): 307-15)
Hypoxia is a condition of the decreasing oxygen supply on the fetal body tissues that will lead to fetal mortality. The experts will categorize fetal condition into two levels i.e. normal and ...hypoxia, based on CTG data analysis. Dataset which contain noises will affect to misinterpretation by the experts. The ensemble learning methods and deep learning methods are implemented to detect hypoxia. Ensemble learning models used include Bagging Tree, AdaBoost, and Vooting Classifier with classifier methods such as Decision Tree, SVM, SGD, GLVQ, and Naive Bayes. Deep learning models used include CNN and DenseNet. These methods are applied to CTG dataset, especially FHR signal. The classification processes utilize pH label as the benchmark. The benchmark is use to classify the dataset into two stage, normal and hypoxia. The best evaluation performance is obtained by Bagging Tree method with Naive Bayes Classifier. The F1-score for normal class was 0.76 and 0.45 for hypoxia class.
Fetal head circumference (HC) is one of the most important biometrics in assessing fetal growth during prenatal ultrasound examinations. However, measuring the fetal head is not an easy task. This ...study aims to create an automatic fetal head measurement system. This system is expected to run on mobile devices as part of telehealth system. HC measurement can be done with object detection method, followed by edge detection, then using every edge pixel, fetal head can be approximated using ellipse fitting. Evaluations are carried out using hit rates and error rates for ellipse fitting. From each method that was tested, the evaluation result showed that the Adaptive Boosting and Fast Ellipse Fitting (ElliFit) method had the best performance. This method also had a relatively fast execution time for a mobile device, which is 3-5 seconds.
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