Defining and Measuring Academic Success

Despite, and perhaps because of its amorphous nature, the term ‘academic success’ is one of the most widely used constructs in educational research and assessment within higher education. This paper conducts an analytic literature review to examine the use and operationalization of the term in multiple academic fields. Dominant definitions of the term are conceptually evaluated using Astin’s I-E-O model resulting in the proposition of a revised definition and new conceptual model of academic success. Measurements of academic success found throughout the literature are presented in accordance with the presented model of academic success. These measurements are provided with details in a user-friendly table (Appendix B). Results also indicate that grades and GPA are the most commonly used measure of academic success. Finally, recommendations are given for future research and practice to increase effective assessment of academic success. Accessed 112,251 times on https://pareonline.net from March 15, 2015 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Neighborhood Cohesion, Neighborhood Disorder, and Cardiometabolic Risk

Perceptions of neighborhood disorder (trash, vandalism) and cohesion (neighbors trust one another) are related to residents’ health. Affective and behavioral factors have been identified, but often in studies using geographically select samples. We use a nationally representative sample (n = 9032) of United States older adults from the Health and Retirement Study to examine cardiometabolic risk in relation to perceptions of neighborhood cohesion and disorder. Lower cohesion is significantly related to greater cardiometabolic risk in 2006/2008 and predicts greater risk four years later (2010/2012). The longitudinal relation is partially accounted for by anxiety and physical activity

Neighborhood Socioeconomic Status and Health: A Longitudinal Analysis

Higher income neighborhoods are associated with better health, a relation observed in many cross-sectional studies. However, prior research focused on the prevalence of health conditions, and examining the incidence of new health conditions may provide stronger support for a potential causal role of neighborhoods on health. We used the 2004 and 2014 waves of the Midlife in the United States Study (n = 1726; ages 34–83) to examine health condition incidence as a function of neighborhood income. Among participants who had lived in the same neighborhood across the time period, we hypothesized that higher neighborhood income would be associated with a lower incidence of health conditions ten years later. Health included 18 chronic conditions related to mental (anxiety, depression) and physical (cardiovascular, immune) health. Multinomial logistic regression analyses adjusting for individual income and sociodemographics indicated that the odds of developing two or more new health conditions (no new health conditions as referent), was significantly lower (OR = 0.92, CI: 0.86, 0.99) for every $10,000 increment in neighborhood income. Associations did not vary by age or neighborhood tenure. Results add to a literature documenting that higher neighborhood income is associated with better health

Neighborhood Features and Physiological Risk: An Examination of Allostatic Load

Poor neighborhoods may represent a situation of chronic stress, and may therefore be associated with health-related correlates of stress. We examined whether lower neighborhood income would relate to higher allostatic load, or physiological well-being, through psychological, affective, and behavioral pathways. Using data from the Biomarker Project of the Midlife in the United States (MIDUS) study and the 2000 Census, we demonstrated that people living in lower income neighborhoods have higher allostatic load net of individual income. Moreover, findings indicate that this relation is partially accounted for by anxious arousal symptoms, fast food consumption, smoking, and exercise habits

Neighborhood Cohesion and Daily Well-Being: Results from a Diary Study

Neighborly cohesiveness has documented benefits for health. Furthermore, high perceived neighborhood cohesion offsets the adverse health effects of neighborhood socioeconomic adversity. One potential way neighborhood cohesion influences health is through daily stress processes. The current study uses participants (n = 2022, age 30–84 years) from The Midlife in the United States II and the National Study of Daily Experiences II, collected between 2004 and 2006, to examine this hypothesis using a within-person, daily diary design. We predicted that people who perceive high neighborhood cohesion are exposed to fewer daily stressors, such as interpersonal arguments, lower daily physical symptoms and negative affect, and higher daily positive affect. We also hypothesized that perceptions of neighborhood cohesion buffer decline in affective and physical well-being on days when daily stressors do occur. Results indicate that higher perceived neighborhood cohesion predicts fewer self-reported daily stressors, higher positive affect, lower negative affect, and fewer physical health symptoms. High perceived neighborhood cohesion also buffers the effects of daily stressors on negative affect, even after adjusting for other sources of social support. Results from the present study suggest interventions focusing on neighborhood cohesion may result in improved well-being and may minimize the adverse effect of daily stressors

Critical linkages between livestock production, livestock trade and potential spread of human African trypanosomiasis in Uganda:Bioeconomic herd modeling and livestock trade analysis

Background: Tsetse-transmitted human African trypanosomiasis (HAT) remains endemic in Uganda. The chronic form caused by Trypanosoma brucei gambiense (gHAT) is found in north-western Uganda, whereas the acute zoonotic form of the disease, caused by T. b. brucei rhodesiense (rHAT), occurs in the eastern region. Cattle is the major reservoir of rHAT in Uganda. These two forms of HAT are likely to converge resulting in a public health disaster. This study examines the intricate and intrinsic links between cattle herd dynamics, livestock trade and potential risk of spread of rHAT northwards. Methods: A bio-economic cattle herd model was developed to simulate herd dynamics at the farm level. Semi-structured interviews (n = 310), focus group discussions (n = 9) and key informant interviews (n = 9) were used to evaluate livestock markets (n = 9) as part of the cattle supply chain analysis. The cattle market data was used for stochastic risk analysis. Results: Cattle trade in eastern and northern Uganda is dominated by sale of draft and adult male cattle as well as exportation of young male cattle. The study found that the need to import draft cattle at the farm level was to cover deficits because of the herd structure, which is mostly geared towards animal traction. The importation and exportation of draft cattle and disposal of old adult male cattle formed the major basis of livestock movement and could result in the spread of rHAT northwards. The risk of rHAT infected cattle being introduced to northern Uganda from the eastern region via cattle trade was found to be high (i.e. probability of 1). Conclusion: Through deterministic and stochastic modelling of cattle herd and cattle trade dynamics, this study identifies critical links between livestock production and trade as well as potential risk of rHAT spread in eastern and northern Uganda. The findings highlight the need for targeted and routine surveillance and control of zoonotic diseases such as rHAT

Livestock network analysis for rhodesiense human African trypanosomiasis control in Uganda

Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness.Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (Ro 1.287), hence regarded as “slow” pathogen, and evaluate the effects of disease interventions.Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network.Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public–private partnerships, would serve to limit its spread