Current trends suggest that significant gender disparities exist within Science, Technology, Engineering, and Mathematics (STEM) education at university, with female students being underrepresented in physics, but more equally represented in life sciences (e.g., biology, medicine). To understand these trends, it is important to consider the context in which students make decisions about which university courses to enrol in. The current study seeks to investigate gender differences in STEM through a unique approach that combines network analysis of student enrollment data with an interpretive lens based on the sociological theory of Pierre Bourdieu. We generate a network of courses taken by around 9000 undergraduate physics students (from 2009 to 2014) to quantify Bourdieu’s concept of field. We identify the fields in which physics students participate by constructing a weighted co-enrollment network and finding communities within it. We then use odds ratios to report gender differences in transverse movements between different academic fields, and non-parametric tests to assess gender differences in vertical movements (changes in students’ achievement rankings within a field). Odds ratios comparing the likelihood of progression from one field to another indicate that female students were more likely to make transverse movements into life science fields. We also found that university physics did a poor job in attracting high achieving students, and especially high achieving female students. Of the students who did choose to study physics at university, low and middle achieving female high school students were more likely to decrease their relative rank in their first year compared to their male counterparts. Low achieving female students were also less likely to continue with physics after their first year compared to their male counterparts. Results and implications are discussed in the context of Bourdieu’s theory, and previous research. We argue that in order to remove constraints on female students’ study choices, the field of physics needs to provide a culture in which all students feel like they belong.

Understanding factors that contribute to students' self-concept in science is an important task in boosting the number of students studying science and retaining students in science fields. A questionnaire was administered to science students at the University of Auckland in New Zealand (N = 693) to test a theoretical model of science self-concept tied to the work of Pierre Bourdieu. In this model, a student's social capital (i.e., relationships with parents, teachers and peers) and cultural capital (i.e., science related resources) are seen as key determinants of a student's belief that science is a domain in which they can succeed. Results from a Structural Equation Model (SEM) show that, of the factors included in the model, exposure to passionate science teachers during high school was the main predictor of science self-concept for our sample of university science students, while having peers who value science was also found to be important. Interestingly, science-related resources and parents' value of science were not significant predictors of science self-concept, but the number of university generations in the family did have a positive association. Students who self-identified as male had higher levels of science self-concept, even after accounting for social and cultural factors in our theoretical model. Implications of these findings are discussed in the context of the field of science education and Bourdieu's sociological theory.

The problem of academic dishonesty is as old as it is widespread – dating back millennia and perpetrated by the majority of students. Attempts to promote academic integrity, by comparison, are relatively new and rare – stretching back only a few hundred years and implemented by a small fraction of schools and universities. However, the past decade has seen an increase in efforts among universities to promote academic integrity among students, particularly through the use of online courses or tutorials. Previous research has found this type of instruction to be effective in increasing students’ knowledge of academic integrity and reducing their engagement in academic dishonesty. The present study contributes to this literature with a natural experiment on the effects of the Academic Integrity Course (AIC) at The University of Auckland, which became mandatory for all students in 2015. In 2012, a convenience sample of students (n = 780) had been asked to complete a survey on their perceptions of the University’s academic integrity polices and their engagement in several forms of academic dishonesty over the past year. In 2017, the same procedures and survey were used to collect data from second sample of students (n = 608). After establishing measurement invariance across the two samples on all latent factors, analysis of variance revealed mixed support for the studies hypotheses. Unexpectedly, students who completed the AIC (i.e., the 2017 sample) reported: (1) significantly lower (not higher) levels of understanding, support, and effectiveness with respect to the University’s academic integrity policies; (2) statistically equivalent (not higher) levels of peer disapproval of academic misconduct, and; (3) significantly higher (not lower) levels of peer engagement in academic misconduct. However, results related to participants’ personal engagement in academic misconduct offered partial support for hypotheses – those who completed the AIC reported significantly lower rates of engagement on three of the eight behaviors included in the study. The implications and limitations of these findings are discussed as well as possible future directions for research.

Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people.Trial registrationClinicaltrials.gov (NCT01038583)

Background The emergence and re-emergence of infectious diseases presents a significant challenge to public health and broader society. This study utilises novel nationwide data to calculate the transmission risk and potential inequity of infectious disease outbreaks through use of network analysis.Methods Nationwide employment and education microdata (»4.7 million individuals in Aotearoa New Zealand) were used to develop the Aotearoa Co-incidence Network (ACN). The ACN considers connections generated when individuals are employed at the same workplaces or enrolled at the same schools. Through forms of network analysis, connections between geospatial areas can be established and provide proxy measures of infectious disease transmission risk. The ACN was also overlayed with nationwide population vulnerability data based on the number of older adults (>65 years) and individuals with long-term health conditions.Findings We identify areas that have both high potential transmission risk (i.e., highly connected) and high vulnerability to infectious diseases. Community detection identified geographic boundaries that can be relevant to the application of regional restrictions for limiting infectious disease transmission.Interpretation Integrating novel network science and geospatial analytics provides a simple way to study infectious disease transmission risk and population vulnerability to outbreaks. Our replicable method has utility for researchers globally with access to such data. It can help inform equitable preparation for, and responses to infectious disease outbreaks.