Medical career expectations of academically talented high school students: A nationwide cross-sectional study in China
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".