From admissions to licensure: education data associations from a multi-centre undergraduate medical education collaboration

This paper reports the findings of a Canada based multi-institutional study designed to investigate the relationships between admissions criteria, in-program assessments, and performance on licensing exams. The study's objective is to provide valuable insights for improving educational practices across different institutions. Data were gathered from six medical schools: McMaster University, the Northern Ontario School of Medicine University, Queen's University, University of Ottawa, University of Toronto, and Western University. The dataset includes graduates who undertook the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1) between 2015 and 2017. The data were categorized into five distinct sections: demographic information as well as four matrices: admissions, course performance, objective structured clinical examination (OSCE), and clerkship performance. Common and unique variables were identified through an extensive consensus-building process. Hierarchical linear regression and a manual stepwise variable selection approach were used for analysis. Analyses were performed on data set encompassing graduates of all six medical schools as well as on individual data sets from each school. For the combined data set the final model estimated 32% of the variance in performance on licensing exams, highlighting variables such as Age at Admission, Sex, Biomedical Knowledge, the first post-clerkship OSCE, and a clerkship theta score. Individual school analysis explained 41-60% of the variance in MCCQE1 outcomes, with comparable variables to the analysis from of the combined data set identified as significant independent variables. Therefore, strongly emphasising the need for variety of high-quality assessment on the educational continuum. This study underscores the importance of sharing data to enable educational insights. This study also had its challenges when it came to the access and aggregation of data. As such we advocate for the establishment of a common framework for multi-institutional educational research, facilitating studies and evaluations across diverse institutions. This study demonstrates the scientific potential of collaborative data analysis in enhancing educational outcomes. It offers a deeper understanding of the factors influencing performance on licensure exams and emphasizes the need for addressing data gaps to advance multi-institutional research for educational improvements.

Factors Associated with Meeting the Psychosocial Needs of Cancer Survivors in Nova Scotia, Canada

Purpose: The purpose of this study is to describe the psychosocial needs of cancer survivors and examine whether sociodemographic factors and health care providers accessed are associated with needs being met. Methods: All Nova Scotia survivors meeting specific inclusion and exclusion criteria are identified from the Nova Scotia Cancer Registry and sent an 83-item survey to assess psychosocial concerns and whether and how their needs were met. Descriptive statistics (frequencies, percentages) and Chi-square analyses are used to examine associations between sociodemographic and provider factors and outcomes. Results: Anxiety and fear of recurrence, depression, and changes in sexual intimacy are major areas of concern for survivors. Various sociodemographic factors, such as immigration status, education, employment, and internet use, are associated with reported psychosocial health and having one’s needs met. Having both a specialist and primary care provider in charge of follow-up care is associated with a significantly (p < 0.05) higher degree of psychosocial and informational needs met compared to only one physician or no follow-up physician in charge. Accessing a patient navigator also is significantly associated with a higher degree of needs met. Conclusions: Our study identifies the most prevalent psychosocial needs of cancer survivors and the factors associated with having a higher degree of needs met, including certain sociodemographic factors, follow-up care by both a primary care practitioner and specialist, and accessing a patient navigator.

Molecular Dynamics Studies of Overbased Detergents on a Water Surface

Molecular dynamics (MD) simulations are reported of model overbased detergent nanoparticles on a model water surface which mimic their behavior on a Langmuir trough or large water droplet in engine oil. The simulations predict that the structure of the nanoparticle on a water surface is different to when it is immersed in a bulk hydrophobic solvent. The surfactant tails are partly directed out of the water, while the carbonate core maximizes its extent of contact with the water. Umbrella sampling calculations of the potential of mean force between two particles showed that they are associated with varying degrees with a maximum binding free energy of ca. 10 k_BT for the salicylate stabilized particle, ca. 8 k_BT for a sulfurized alkyl phenate stabilized particle, and ca. 5 k_BT for a sulfonate stabilized particle. The differences in the strength of attraction depend on the proximity of nearest approach and the energy penalty associated with the disruption of the hydration shell of water molecules around the calcium carbonate core when the two particles approach. This is greatest for the sulfonate particle, which partially loses the surfactant ions to the solution, and least for the salicylate, which forms the weakest water "cage". The particles are separated by a water hydration layer, even at the point of closest approach.

Role of Deprotonation Free Energies in pKa Prediction and Molecule Ranking

A computationally efficient classical molecular simulation technique is derived for ranking the pKa values of a set of chemically similar congeneric molecules in an implicit solvent model of water. This uses the deprotonation free energy of the titratable group in the gas and aqueous phases obtained by thermodynamic integration (TI). For a series of alcohols and acids a strong linear correlation is demonstrated between the experimental pKa and the deprotonation free energy difference in the gas and liquid phases. These calculations also show that classical TI is more efficient than slow-growth TI in calculating deprotonation free energies for the series of molecules considered herein.