Kulamakan Kulasegaram’s research while affiliated with University of Toronto and other places

Holistic review has been widely adopted in medical education as a means of promoting equity in the application process and diversity in the medical workforce. Artificial intelligence (AI) is a rapidly emerging technology already having an impact on the medical school and residency application process as students and faculty alike increasingly turn to AI tools to automate some steps in the preparation and evaluation of application materials. While AI may have the potential to improve the holistic admissions process by increasing efficiency and adding some measure of standardization among reviewers, the authors caution that this promise does not come without certain pitfalls. AI models may introduce new sources of bias and amplify existing ones, which when combined with a lack of transparency regarding their use in the admissions process, may perpetuate the very inequities that holistic review seeks to minimize. The authors call for the medical education community to establish clear regulations to govern the acceptable use of AI in the admissions process and for a principled adoption of AI tools in a way that is sustainable for applicants and reviewers in the future.

Purpose To generate Cronbach’s alpha and further mixed methods construct validity evidence for the Blended Learning Usability Evaluation–Questionnaire (BLUE-Q). Methods Forty interprofessional clinicians completed the BLUE-Q after finishing a 3-month long blended learning professional development program in Ontario, Canada. Reliability was assessed with Cronbach’s α for each of the 3 sections of the BLUE-Q and for all quantitative items together. Construct validity was evaluated through the Grand-Guillaume-Perrenoud et al. framework, which consists of 3 elements: congruence, convergence, and credibility. To compare quantitative and qualitative results, descriptive statistics, including means and standard deviations for each Likert scale item of the BLUE-Q were calculated. Results Cronbach’s α was 0.95 for the pedagogical usability section, 0.85 for the synchronous modality section, 0.93 for the asynchronous modality section, and 0.96 for all quantitative items together. Mean ratings (with standard deviations) were 4.77 (0.506) for pedagogy, 4.64 (0.654) for synchronous learning, and 4.75 (0.536) for asynchronous learning. Of the 239 qualitative comments received, 178 were identified as substantive, of which 88% were considered congruent and 79% were considered convergent with the high means. Among all congruent responses, 69% were considered confirming statements and 31% were considered clarifying statements, suggesting appropriate credibility. Analysis of the clarifying statements assisted in identifying 5 categories of suggestions for program improvement. Conclusion The BLUE-Q demonstrates high reliability and appropriate construct validity in the context of a blended learning program with interprofessional clinicians, making it a valuable tool for comprehensive program evaluation, quality improvement, and evaluative research in health professions education.

Purpose The development of the Diabetic Wound Assessment Learning Tool (DiWALT) has previously been described. However, an examination of its application to a larger, more heterogeneous group of participants is lacking. In order to allow for a more robust assessment of the psychometric properties of the DiWALT, we applied it to a broader group of participants. Materials and Methods We built validity evidence for the tool by assessing 74 clinician participants' during two simulated wound care scenarios: Two assessors independently rated each participant using our tool, with a total of five raters providing scores. We evaluated validity evidence using generalizability theory analyses and by comparing performance scores across the three experience levels using ANOVA. Results The tool differentiated between novices and the other two groups well ( p < 0.01) but not between intermediates and experts ( p = 0.34). Our generalizability coefficient was 0.87, and our phi coefficient was 0.87. Conclusion The accumulated validity evidence suggests our tool can be used to assess novice clinicians' competence in initial diabetic wound management during simulated cases. Further work is required to clarify the DiWALT's performance in a broader universe of generalisation and to examine evidence for its extrapolation and implications inferences.

The Africa Health Collaborative (AHC) initiative embarked on a transformative ten-year collaboration with Kwame Nkrumah University of Science and Technology (KNUST) and the University of Toronto (U of T) to co-create continuing education programs geared toward augmenting the proficiency of primary care practitioners in Ghana. While upholding core principles within the AHC framework, emphasizing respect, inclusivity, equity, reciprocity, ethics, dynamism, and stewardship, seven teams of U of T and KNUST faculty engaged in collaborative efforts to design, administer, and evaluate five in-person “short courses” in Ghana on Palliative Care, Quality Improvement for Health Professionals, Prehospital Emergency Care, Community Emergency Care, and Emergency Preparedness and Response to Epidemic-Prone Diseases to approximately 100 Ghanaian primary care professionals. This paper describes a model of co-creation, highlights lessons learned from a robust evaluation process, and proposes that this co-creation model can strengthen primary health care in Ghana and ultimately transform health systems in Africa.

Purpose Along with other industries, healthcare is becoming increasingly digitized. Our study explores how the field of academic medicine is preparing for this digital future. Method Active strategic plans available in English were collected from faculties of medicine in Canada (n = 14), departments in medical schools (n = 17), academic health science centres (n = 23) and associated research institutes (n = 5). In total, 59 strategic plans were subjected to a practice-oriented form of document analysis, informed by the concept of sociotechnical imaginaries. Results On the one hand, digital health is discursively treated as a continuation of the academic medicine vision, with expansions of physician competencies and of research institutes contributions. These imaginaries do not necessarily disrupt the field of academic medicine as currently configured. On the other hand, there is a vision of digital health pursuing a robust sociotechnical future with transformative implications for how care is conducted, what forms of knowledge are prioritized, how patients and patienthood will be understood, and how data work will be distributed. This imaginary may destabilize existing distributions of knowledge and power. Conclusions Looking through the lens of sociotechnical imaginaries, this study illuminates strategic plans as framing desirable futures, directing attention towards specific ways of understanding problems of healthcare, and mobilizing the resources to knit together social and technical systems in ways that bring these visions to fruition. There are bound to be tensions as these sociotechnical imaginaries are translated into material realities. Many of those tensions and their attempted resolutions will have direct implications for the expectations of health professional graduates, the nature of clinical learning environments, and future relationships with patients. Sociology of digital health and science and technology studies can provide useful insights to guide leaders in academic medicine shaping these digital futures.

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.

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare.

Purpose Along with other industries, healthcare is becoming increasingly digitized. Our study explores how the field of academic medicine is preparing for this digital future. We conducted an analysis of strategic plans from two organizational forms that shape the field of academic medicine: medical schools and academic health science centres. Method This study was conducted in 2023. Active strategic plans available in English were collected from faculties of medicine in Canada (n = 14), departments in medical schools (n = 17), academic health science centres (n = 23) and associated research institutes (n = 5). In total, 59 strategic plans were subjected to a practice-oriented form of document analysis, informed by the concept of sociotechnical imaginaries. Results While both organizational types are attending to the digital future of healthcare, this future seems to be under-specified in the strategic planning of medical schools. In contrast, academic health science centres are pursuing a robust sociotechnical future with transformative implications for how care is conducted, what forms of knowledge are prioritized, how patients and patienthood will be understood, and how data work will be distributed. Conclusions Looking through the lens of sociotechnical imaginaries, this study illuminates strategic plans as framing desirable futures, directing attention towards specific ways of understanding problems of healthcare, and mobilizing the resources to knit together social and technical systems in ways that bring these visions to fruition. There are bound to be tensions as these sociotechnical imaginaries are translated into material realities. Many of those tensions and their attempted resolutions will have direct implications for the expectations of health professional graduates, the nature of clinical learning environments, and future relationships with patients. Sociology of digital health and science and technology studies can provide useful insights to guide leaders in academic medicine shaping these digital futures.

Introduction Test‐enhanced learning (TEL) is an impactful teaching and learning strategy that prioritises active learner engagement through the process of regular testing and reviewing. While it is clear that meaningful feedback optimises the effects of TEL, the ideal timing of this feedback (i.e. immediate or delayed) in a medical education setting is unclear. Method Forty‐one second‐year medical students were recruited from the University of Melbourne. Participants were given a multiple‐choice question test with a mix of immediate (i.e. post‐item) and delayed (i.e. post‐item‐block) conceptual feedback. Students were then tested on near and far transfer items during an immediate post‐test, and at a one‐week follow‐up. Results A logistic mixed effects model was used to predict the probability of successful near and far transfer. As expected, participants in our study tended to score lower on far transfer items than they did on near transfer items. In addition, correct initial response on a parent question predicted subsequent correct responding. Contrary to our hypotheses, the feedback timing effect was non‐significant—there was no discernible difference between feedback delivered immediately versus delayed feedback. Discussion The findings of this study suggest that the timing of feedback delivery (post‐item versus post‐item‐block) does not influence the efficacy of TEL in this medical education setting. We therefore suggest that educators may consider practical factors when determining appropriate TEL feedback timing in their setting.