There is increasing interest in understanding how active learning affects students’ mental health as science courses transition from traditional lecture to active learning. Prior research has found that active learning can both alleviate and exacerbate undergraduate mental health problems. Existing studies have only examined the relationship between active learning and anxiety. No studies have examined the relationship between active learning and undergraduate depression. To address this gap in the literature, we conducted hour-long exploratory interviews with 29 students with depression who had taken active learning science courses across six U.S. institutions. We probed what aspects of active learning practices exacerbate or alleviate depressive symptoms and how students’ depression affects their experiences in active learning. We found that aspects of active learning practices exacerbate and alleviate students’ depressive symptoms, and depression negatively impacts students’ experiences in active learning. The underlying aspects of active learning practices that impact students’ depression fall into four overarching categories: inherently social, inherently engaging, opportunities to compare selves to others, and opportunities to validate or invalidate intelligence. We hope that by better understanding the experiences of undergraduates with depression in active learning courses we can create more inclusive learning environments for these students.
Mental health conditions can impact college students’ social and academic achievements. As such, students may disclose mental illnesses on medical school applications. Yet, no study has investigated to what extent disclosure of a mental health condition impacts medical school acceptance. We designed an audit study to address this gap. We surveyed 99 potential admissions committee members from at least 43 unique M.D.-granting schools in the U.S. Participants rated a fictitious portion of a medical school application on acceptability, competence, and likeability. They were randomly assigned to a condition: an application that explained a low semester GPA due to a mental health condition, an application that explained a low semester GPA due to a physical health condition, or an application that had a low semester GPA but did not describe any health condition. Using ANOVAs, multinomial regression, and open-coding, we found that committee members do not rate applications lower when a mental health condition is revealed. When asked about their concerns regarding the application, 27.0% of participants who received an application that revealed a mental health condition mentioned it as a concern; 14.7% of participants who received an application that revealed a physical health condition mentioned it as a concern. Committee members were also asked about when revealing a mental health condition would be beneficial and when it would be detrimental. This work indicates that medical school admissions committee members do not exhibit a bias towards mental health conditions and provides recommendations on how to discuss mental illness on medical school applications.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
Motivated by the fact that understanding the dynamics of disease vector is crucial to understanding the transmission and control of the VBDs they cause, a novel weather-driven deterministic model for the population biology of the mosquito is formulated and rigorously analyzed. Numerical simulations, using relevant weather and entomological data for Anopheles mosquito (the vector for malaria), show that maximum mosquito abundance occurs when temperature and rainfall values lie in the range [20-25]C and [105-115] mm, respectively.
The Anopheles mosquito ecology model is extended to incorporate human dynamics. The resulting weather-driven malaria transmission model, which includes many of the key aspects of malaria (such as disease transmission by asymptomatically-infectious humans, and enhanced malaria immunity due to repeated exposure), was rigorously analyzed. The model which also incorporates the effect of diurnal temperature range (DTR) on malaria transmission dynamics shows that increasing DTR shifts the peak temperature value for malaria transmission from 29C (when DTR is 0C) to about 25C (when DTR is 15C).
Finally, the malaria model is adapted and used to study the transmission dynamics of chikungunya, dengue and Zika, three diseases co-circulating in the Americas caused by the same vector (Aedes aegypti). The resulting model, which is fitted using data from Mexico, is used to assess a few hypotheses (such as those associated with the possible impact the newly-released dengue vaccine will have on Zika) and the impact of variability in climate variables on the dynamics of the three diseases. Suitable temperature and rainfall ranges for the maximum transmission intensity of the three diseases are obtained.