METHODS: Ethical and institutional approval was obtained at each study location. A questionnaire was designed and distributed to final year students. Domains assessed included demographics, career plans and reasons associated. Anonymised responses were collated and evaluated. Categorical data were compared with Fisher's exact test.
RESULTS: Responses were obtained from 342 students in four medical schools of whom 78.6% were undergraduates. Over half (53%) were Irish, with Malaysia, Canada and the USA the next most common countries of origin. Only 18% of students intended to pursue surgery, with 60% stating they did not plan to, and 22% undecided. Of those who plan not to pursue surgery, 28% were unsure about a speciality but the most common choices were medicine (39%), general practice (16%) and paediatrics (8%). Reasons for not picking a career in surgery included long hours and the unstructured career path. Suggestions to improve uptake included earlier and more practical exposure to surgery, improved teaching/training and reduction in working hours.
CONCLUSIONS: In this study 18% of final year medical students identified surgery as their chosen career pathway. Although lifestyle factors are significant in many students' decision, perceived quality and duration of surgical training were also relevant and are modifiable factors which, if improved could increase interest in surgery as a career.
METHODS: Data for 91 countries were obtained from United Nations agencies. The response variable was life expectancy, and the determinant factors were demographic events (total fertility rate and adolescent fertility rate), socioeconomic status (mean years of schooling and gross national income per capita), and health factors (physician density and human immunodeficiency virus [HIV] prevalence rate). Path analysis was used to determine the direct, indirect, and total effects of these factors on life expectancy.
RESULTS: All determinant factors were significantly correlated with life expectancy. Mean years of schooling, total fertility rate, and HIV prevalence rate had significant direct and indirect effects on life expectancy. The total effect of higher physician density was to increase life expectancy.
CONCLUSIONS: We identified several direct and indirect pathways that predict life expectancy. The findings suggest that policies should concentrate on improving reproductive decisions, increasing education, and reducing HIV transmission. In addition, special attention should be paid to the emerging need to increase life expectancy by increasing physician density.