Predicting Young Imposter Syndrome Using Ensemble Learning

Impostor phenomenon: Prevalence among 1st year medical students and strategies for mitigation

Impostor phenomenon (IP) is described as a pattern typified by doubting one's accomplishments and a persistent fear of being exposed as a fraud. These feelings of self-doubt are pervasive along the medical education continuum, beginning with medical students where IP has been associated with emotional stress, physical exhaustion, depression, and anxiety. We, therefore, conducted an interactive workshop with first-year medical students to educate them about the manifesting patterns and risk factors of IP and strategies to mitigate these feelings. The 60-min workshop began with participants voluntarily completing the Young Imposter Scale (YIS) followed by an interactive presentation that reviewed the literature related to IP and its prevalence in medicine. Participants were then assigned to small groups where they discussed three cases of IP in academia and the medical profession. Medical school faculty acted as facilitators and utilized pre-designed prompt questions to stimulate discussion. Students re-convened for a large group report out, where each group shared main discussion points. The session ended with facilitators discussing IP mitigation strategies that can be implemented at the individual, peer, and institutional levels. Participants were also invited to complete a post-workshop evaluation. Fifty first-year medical students participated in the session. A total of 49 (96 %) completed the YIS and post-workshop evaluation. Nineteen (40 %) participants obtained scores on the YIS to indicate a positive finding of IP. The percentage of female medical students meeting the threshold for IP was significantly higher (84 %, n = 41 vs 16 %, n = 7) than male medical students. The workshop was effective at identifying IP and associated risk factors and providing mitigation strategies, with 95.8 % of participants agreeing or strongly agreeing. In qualitative feedback, participants reported that the workshop was "very interactive", "provided strategies to manage impostor syndrome" and "helped me become more vulnerable with my peers." This workshop provided a novel interactive and effective method to increase medical students' awareness about IP which can be employed as a strategy to enhance student's wellness.

A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women

Domestic violence against women is a prevalent in Liberia, with nearly half of women reporting physical violence. However, research on the biosocial factors contributing to this issue remains limited. This study aims to predict women's vulnerability to domestic violence using a machine learning approach, leveraging data from the Liberian Demographic and Health Survey (LDHS) conducted in 2019-2020. We employed seven machine learning algorithms to achieve this goal, including ANN, KNN, RF, DT, XGBoost, LightGBM, and CatBoost. Our analysis revealed that the LightGBM and RF models achieved the highest accuracy in predicting women's vulnerability to domestic violence in Liberia, with 81% and 82% accuracy rates, respectively. One of the key features identified across multiple algorithms was the number of people who had experienced emotional violence. These findings offer important insights into the underlying characteristics and risk factors associated with domestic violence against women in Liberia. By utilizing machine learning techniques, we can better predict and understand this complex issue, ultimately contributing to the development of more effective prevention and intervention strategies.

A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries

BACKGROUND

The proper intake of nutrients is essential to the growth and maturation of youngsters. In sub-Saharan Africa, 1 in 7 children dies before age 5 y, and more than a third of these deaths are attributed to malnutrition. The main purpose of this study was to develop a majority voting-based hybrid ensemble (MVBHE) learning model to accelerate the prediction accuracy of malnutrition data of under-five children in sub-Saharan Africa.

METHODS

This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algorithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model.

RESULTS

We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%).

CONCLUSIONS

The random forest algorithm demonstrated the highest prediction accuracy (81%) compared with the decision tree, eXtreme Gradient Boosting, and k-nearest neighbors algorithms. The accuracy was then enhanced to 96% using the MVBHE model. The MVBHE model is recommended by the present study as the best way to predict malnutrition in under-five children.

A rapid review of machine learning approaches for telemedicine in the scope of COVID-19

The COVID-19 pandemic has rapidly spread around the world. The rapid transmission of the virus is a threat that hinders the ability to contain the disease propagation. The pandemic forced widespread conversion of in-person to virtual care delivery through telemedicine. Given this gap, this article aims at providing a literature review of machine learning-based telemedicine applications to mitigate COVID-19. A rapid review of the literature was conducted in six electronic databases published from 2015 through 2020. The process of data extraction was documented using a PRISMA flowchart for inclusion and exclusion of studies. As a result, the literature search identified 1.733 articles, from which 16 articles were included in the review. We developed an updated taxonomy and identified challenges, open questions, and current data types. Our taxonomy and discussion contribute with a significant degree of coverage from subjects related to the use of machine learning to improve telemedicine in response to the COVID-19 pandemic. The evidence identified by this rapid review suggests that machine learning, in combination with telemedicine, can provide a strategy to control outbreaks by providing smart triage of patients and remote monitoring. Also, the use of telemedicine during future outbreaks could be further explored and refined.