Table 2.

Mixed multinomial logit regression results*

Explanatory variable	Model 1	Model 2	Model 3	Model 4
1. Probability of obtaining employment†	1.324 (1.263 to 1.388)	1.346 (1.278 to 1.418)	1.339 (1.271 to 1.412)	1.337 (1.272 to 1.405)
2. Lifestyle and work hours	0.905 (0.867 to 0.944)	0.901 (0.852 to 0.952)	0.891 (0.843 to 0.943)	0.907 (0.860 to 0.957)
Population SD		0.288 (0.161 to 0.415)	0.282 (0.155 to 0.409)	0.301 (0.182 to 0.419)
3. Recognition by patients	1.105 (1.055 to 1.157)	1.118 (1.064 to 1.176)	1.116 (1.061 to 1.173)	1.098 (1.047 to 1.151)
4. Prestige among colleagues	1.082 (1.024 to 1.143)	1.096 (1.033 to 1.163)	1.110 (1.046 to 1.179)	1.062 (1.005 to 1.121)
5. Opportunity for professional development	1.326 (1.254 to 1.403)	1.347 (1.267 to 1.432)	1.347 (1.265 to 1.433)	1.303 (1.229 to 1.381)
6. Annual remuneration with 10–15 years’ experience‡	0.821 (0.782 to 0.863)	0.812 (0.770 to 0.856)	1.062 (0.701 to 1.610)
Interaction: female gender			0.884 (0.817 to 0.957)
Interaction: concordance with favourite specialty§			0.885 (0.815 to 0.962)
Interaction: age (years)			0.995 (0.978 to 1.012)
7. Proportion of compensation from private practice†	1.195 (1.139 to 1.255)	1.210 (1.148 to 1.276)	1.218 (1.154 to 1.285)	1.071 (1.028 to 1.116)
Number of respondents¶	836	836	818	887
Number of observations**	4839	4839	4738	5184

*The coefficients represent the effect of a unit change in the independent variable on the odds of preferring a specialty. Numbers in brackets below each coefficient are 95% CI.

†Attribute values normalised to range from 0 to 10, so that each unit corresponds to 10%.

‡Attribute values normalised so that each unit corresponds to €10 000.

§Binary variable equal to 1 when the student's preferred specialty is also his favourite specialty.

¶Number of students with data on all explanatory variables in the model.

**Number of specialty choices with data on all explanatory variables in the model.