Medical career expectations of academically talented high school students: A nationwide cross-sectional study in China
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Abstract Background: Academically talented high school students (ATHSSs), an exceptional cohort, are not well studied for their career expectations, especially for those with medical career expectation (MCE). Nowadays, the public perception of the medical profession is changing in China. The purpose of this study was to answer questions about ‘is medicine attractive for ATHSSs and ‘what factors affect medical career expectations (MCE) for ATHSSs’ in China. Methods: A total of 16,479 representative ATHSSs in senior three completed a questionnaire and four different academic tests. Frequency statistics showed the proportion of ATHSSs with MCE. Unpaired t-tests were performed to find out the differences in demographics, family background, and academic performance between students with and without MCE. The logit models analysis were applied to explore the potential factors that affected the MCE of this exceptional group of students. Results: ATHSSs with MCE accounted for 20.6% (ranking 7/18) of the respondents. They were more likely to be female, came from relatively poorer families, lived in a rural area, and performed significantly worse in all academic tests except for mathematics, compared with those without MCE. In addition, the results revealed that gender (β=-0.436, p<0.01), region of hometown (β=-103, p<0.1), mother’s years of schooling (β=-0.019, p<0.05), and father’s occupational status (β=-0.005, p<0.01) contributed significantly to the MCE of academically talented students. Better performance in mathematics affected the MCE of ATHSSs taking the liberal arts and science tests differently. Conclusions: We found the medical career is becoming unattractive to academically talented students and the medical career may be loosing their aura in China. Students who have medical career expectations are likely to be females and to have a weak family background. We discuss implications for medical education.Keywords:
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When outcome variables are ordinal rather than continuous, the ordered logit model, aka the proportional odds model (ologit/po), is a popular analytical method. However, generalized ordered logit/partial proportional odds models (gologit/ppo) are often a superior alternative. Gologit/ppo models can be less restrictive than proportional odds models and more parsimonious than methods that ignore the ordering of categories altogether. However, the use of gologit/ppo models has itself been problematic or at least sub-optimal. Researchers typically note that such models fit better but fail to explain why the ordered logit model was inadequate or the substantive insights gained by using the gologit alternative. This paper uses both hypothetical examples and data from the 2012 European Social Survey to address these shortcomings.
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This chapter contains sections titled: Logit Models for Nominal Responses Cumulative Logit Models for Ordinal Responses Paired-Category Ordinal Logits Tests of Conditional Independence Problems
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Financial Distress
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[Objective] To explore the application of cumulative logit model on the frequencies and severity of health risk behaviors (HRB) in students, and compare with Chi-square test for contingency table. [Methods] To analyze the frequencies and severity of HRB in 10 235 students and the difference between male and female, junior school and high school/college students by using cumulative logit model. [Results] The severity of HRB was 1.5-fold higher in male than that in female. The severity of HRB in junior school students was a little higher than that in high school/college students. The frequencies of HRB in male were more than that in female. The frequencies of HRB in junior school were less than that in high school/college. The results from cumulative logit model were more clear, reasonable and quantitative than those from Chi-square test. [Conclusion] The data of ordinal contingency table can be analyzed by using cumulative logit model. The syntax commands of the model are simple and the results are more reasonable and quantitative than Chi-square test. And the results can show the quantitative evaluation of influence factors (values OR), etc.
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Abstract In this article, we study firm-level determinants of corruption using a sample of 164,000 companies from 144 countries for the 2005–2020 period. We analyze two variables related to corruption: the perception of corruption as an obstacle to doing business using an ordinal logit model and actual bribe tax payments using a fractional logit model. Controlling for other factors, both sets of our empirical results show that the extent of corruption is related to the time spent dealing with regulations and inspections. We argue that firms which spend more time dealing with administrative procedures have a greater perception of corruption and are forced to make significantly higher bribe payments. Therefore, in a successful fight against corruption, it is essential to simplify administrative procedures by reducing their number and eliminating direct contacts between firms and officials.
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This paper focuses on persistent poverty in Russia and determines the main micro and macro factors that cause a household to be persistently poor. As potential correlators of persistent poverty, main demographic, socio-economic and regional characteristics of a household, as well as the characteristics of the household's head are considered. The research was carried out on the data of the Russian Longitudinal Monitoring Survey (rounds 59). As an econometric tool, ordered logit analysis was used. A separate section of the paper is devoted to an investi-gation of the main determinants of entry and exit into and from poverty, for which both logit and ordered logit models were used.
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omodel.ado and omodel.hlp can be used to perform a test of the assumption of constancy of effects across categories assumed in models fitted by ologit or oprobit. omodel is an alternative to ologit and oprobit for estimating ordered logit and probit models. It produces the same results but it also reports an approximate likelihood-ratio test of whether the coefficients are equal across categories (i.e. a test of the proportional-odds assumption if logit is the requested model).
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Objective To investigate the correct application of cumulative odds logit model on the ordinal data.Methods The cumulative odds logit model and partial proportional odds model were used for the data from a cohort study in Shaanxi Province,and the results were compared.Results The cumulative odds logit model might not be appropriate when the proportional odds assumption was violated.Conclusion The application of cumulative odds logit model is dependent on certain conditions.It is advisable to adopt other methods when the condition of cumulative odds logit model were violated.
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omodel.ado and omodel.hlp can be used to perform a test of the assumption of constancy of effects across categories assumed in models fitted by ologit or oprobit. omodel is an alternative to ologit and oprobit for estimating ordered logit and probit models. It produces the same results but it also reports an approximate likelihood-ratio test of whether the coefficients are equal across categories (i.e. a test of the proportional-odds assumption if logit is the requested model).
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