Eray Yilmaz

Background: Access to mentorship and interview preparation resources remains inconsistent across Canadian medical schools, potentially disadvantaging students applying to diagnostic radiology. The Canadian Association of Radiologists’ Medical Student Network (CAR MSN) hosts an annual virtual mock interview program pairing fourth-year and third-year medical students with diagnostic radiology residents for structured practice interviews and individualized feedback. Methods: This study used a pre- and post-survey design to evaluate the program’s impact on self-reported interview preparedness across 5 annual cycles (2022-2026). A total of 315 registrants from all 17 Canadian medical schools completed pre-event surveys, and 157 participated in the mock interviews. Paired pre- and post-event data were available from 54 participants and their level of preparedness for the Canadian Resident Matching Service (CaRMS) interviews was analyzed using the Wilcoxon signed-rank test. Results: Self-reported preparedness scores improved significantly, with median scores increasing from 3 (IQR 2-3) to 4 (IQR 4-4) on a 5-point Likert scale ( r = .85, P < .001). However, a 65.6% attrition rate was observed between surveys. Participants rated the mock interview as highly helpful, and 96.3% felt the questions were representative of CaRMS interviews. General perceived stressors related to the unfamiliarity with the programs and the interview process decreased after participation. Conclusion: Despite the attrition rate, these findings suggest that nationally accessible, resident-led mock interview programs can meaningfully improve interview preparedness and confidence for radiology applicants. Future implementation of mentorship-based initiatives may promote equitable access to radiology-specific career guidance.

Background: Understanding how governance factors such as democracy and corruption impact the healthcare workforce is crucial for achieving Universal Health Coverage (UHC). Effective health workforce planning and resource allocation are influenced by these political constructs. This study examines the relationship between democracy and corruption and key healthcare workforce metrics. Methods: A cross-sectional study was conducted using a global dataset from 2020 to 2022. The primary outcome was Physician Density (medical doctors per 10000 people). Secondary outcomes included the generalist to specialist ratio and the percentage of female physicians (% Female). Partial correlations, multivariate analysis of variance (MANOVA), and univariate analysis of variance (ANOVA) were used to analyze the relationship between workforce variables and the democracy index (DI), and corruption perception index (CPI), controlling for domestic health expenditure. Results: Data from 134 countries showed significant positive associations between both DI (r = 0.32, p = 0.004) and CPI (r = 0.43, p < 0.001) with physician density. MANOVA indicated significant multivariate effects of DI (Wilks’ Lambda = 0.8642, p = 0.013) and CPI (Wilks’ Lambda = 0.8036, p = 0.001) on the combined workforce variables. Univariate ANOVAs showed that DI (F = 6.13, p = 0.015) and CPI (F = 10.57, p = 0.002) significantly affected physician density, even after adjusting for domestic expenditure (F = 18.53, p < 0.001). However, neither DI nor CPI significantly impacted the Generalist to Specialist Ratio or % Female Physicians. Discussion: Higher levels of democracy and lower levels of corruption are associated with a greater density of medical doctors, independent of healthcare spending. Policymakers must advocate for governance reforms that support a robust healthcare workforce to support aim of universal health coverage.