factors affecting the service delivery locations of newly graduated iranian general practitioners

conclusions to resolve the unequal geographic distribution of physicians, it is necessary to consider the key factors affecting the attractiveness of a district for gps and the existing demand for health services. attempting to resolve the developmental inequalities among different districts, as well as implementing motivational policies to encourage physicians to work in less developed districts, is recommended. background many factors affect physicians’ dispersion across various locations in iran. identifying these factors can be effective in planning for a fair distribution of physicians throughout geographical areas. objectives this study aims to identify the key factors affecting newly graduated general practitioners’ (gps’) choice of service delivery in iran in regard to location. methods this was a mixed-method, applied study using a descriptive survey. a questionnaire was designed based on key variables extracted from relevant literature and analyzed from interviews with experts in the field. we used expert opinions to assess the questionnaire’s validity, and cronbach’s coefficient alpha was calculated (0.925) to confirm its reliability. the questionnaire was randomly distributed among 400 participants (gps) applying for their mandatory service in iran during four periods in 2015 (100 participants were surveyed in each period). the data was analyzed using the 8th edition of spss and amos, and both exploratory and confirmatory factor analysis methods were applied. results forty key factors were identified loading on five main factors during efa, which explained 77.90% of the total variance. we used the following designations for the five factors: 1) socioeconomic developmental status, 2) geographical conditions, 3) personal reasons, 4) district health services, and 5) demographics. in cfa, the third factor has the highest weight with 0.97, and the first factor has the lowest weight with 0.89. but the slight differences among factor load show the importance of all factors. the highest weighted factor relates to a number of health center variables, such as the type of roads in the district, social development and liberation, physician efficiency in the region, and distance from the provincial capital.